[ { "id": "https://openalex.org/W4383108667", "doi": "10.1109/icra48891.2023.10160821", "title": "Noise and Environmental Justice in Drone Fleet Delivery Paths: A Simulation-Based Audit and Algorithm for Fairer Impact Distribution", "year": 2023, "publication_date": "2023-05-29", "type": "conference-paper", "language": "en", "authors": [ "Zewei Zhou", "Martim Brandão" ], "first_author": "Zewei Zhou", "venue": null, "publisher": null, "abstract": "Despite the growing interest in the use of drone fleets for delivery of food and parcels, the negative impact of such technology is still poorly understood. In this paper we investigate the impact of such fleets in terms of noise pollution and environmental justice. We use simulation with real population data to analyze the spatial distribution of noise, and find that: 1) noise increases rapidly with fleet size; and 2) drone fleets can produce noise hotspots that extend far beyond warehouses or charging stations, at levels that lead to annoyance and interference of human activities. This, we will show, leads to concerns of fairness of noise distribution. We then propose an algorithm that successfully balances the spatial distribution of noise across the city, and discuss the limitations of such purely technical approaches. We complement the work with a discussion of environmental justice, showing how careless UAV fleet development and regulation can lead to reinforcing well-being deficiencies of poor and marginalized communities.", "keywords": [ "Drone", "Noise pollution", "Noise (video)", "Computer science", "Distribution (mathematics)", "Audit", "Complement (music)", "Algorithm", "Operations research", "Noise reduction", "Business", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icra48891.2023.10160821" }, { "id": "https://openalex.org/W4282833220", "doi": "10.1177/03611981221093620", "title": "Using Large-Scale Drone Data to Monitor and Assess the Behavior of Freight Vehicles on Urban Level", "year": 2022, "publication_date": "2022-06-14", "type": "article", "language": "en", "authors": [ "Allister Loder", "Thomas G. Otte", "Klaus Bogenberger" ], "first_author": "Allister Loder", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "Compared with public transport operations, urban freight traffic and its associated delivery operations seem to be frequently overlooked in urban traffic management and traffic flow theory. One explanation for this is certainly the lack of available data, as the competitive freight transport market is fragmented and several actors are unwilling to collect or share tactical and operational data. In this study, we use the unique pNEUMA drone data set from Athens, Greece, to shed light on urban freight operations. We discuss macroscopic traffic indicators in a multimodal context. As the vehicle stopping behavior can adversely influence traffic flow, we reveal the stopping behavior of the different modes represented in the data set using clustering techniques. We find that urban freight vehicles’ stopping frequency lies between the stopping frequencies of cars and buses. We reveal the distribution of stopping times for loading and unloading stops in Athens to have a mean of around 380 seconds. Clustering all loading and unloading stops further reveals three groups of loading and unloading stops that could be labeled by incorporating knowledge and expertise about local particularities. The limited flight time of drones, owing to their battery capacities, did not allow reconstruction of longer vehicle routes, such as an entire vehicle tour within the network. However, this could be addressed in future research by realizing continuous large-scale monitoring routines. The revealed vehicle behavior parameters can be used in traffic models to generate further insights into the impacts of urban freight transport to inform public sector decision makers.", "keywords": [ "Drone", "Context (archaeology)", "Transport engineering", "Cluster analysis", "Traffic flow (computer networking)", "Scale (ratio)", "Public transport", "Set (abstract data type)", "Computer science", "Traffic management", "Flow network", "Operations research" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/03611981221093620" }, { "id": "https://openalex.org/W4388820875", "doi": "10.1109/icnsc58704.2023.10318991", "title": "Vessel-UAV Collaborative Routing Problem for Offshore Oil and Gas Fields Inspection", "year": 2023, "publication_date": "2023-10-25", "type": "conference-paper", "language": "en", "authors": [ "Yantong Li", "Xingqi Wang", "Shuai Zhang", "Shanshan Zhou" ], "first_author": "Yantong Li", "venue": null, "publisher": null, "abstract": "This paper proposes a new mode of vessel-UAV collaborative inspection to address the challenges of high cost, low frequency, and high risk in the traditional offshore oilfield inspection. In the studied problem, a patrol vessel with a UAV departs from the port, sails to a specific location, releases the UAV for oilfield inspection, and retrieves the UAV at another location after the inspection. Considering the spatio-temporal cooperation between ships and UAVs and the variation of endurance capability of UAVs, the collaborative inspection routing problem of \"single vessel and UAV take off to visit multiple target points\" is studied, with a target of minimizing the total variable cost of the vessel and UAV and the sailing time of the vessel. A mixed integer second-order cone programming model based on the time period and the moment of time was established, respectively, to calculate the objective function value of the inspection routing cost. The commercial solver CPLEX is used to solve the proposed models. Numerical experiments based on actual and randomly generated cases are conducted to verify the efficiency of the model. In addition, sensitivity analysis is performed in our paper, including UAV endurance and speed, which can inspire managers for scheduling optimization in practice.", "keywords": [ "Offshore oil and gas", "Submarine pipeline", "Routing (electronic design automation)", "Marine engineering", "Petroleum engineering", "Fossil fuel", "Computer science", "Environmental science", "Geology", "Oceanography", "Engineering", "Computer network" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icnsc58704.2023.10318991" }, { "id": "https://openalex.org/W4392404357", "doi": "10.14569/ijacsa.2024.0150298", "title": "An Algorithm Based on Priority Rules for Solving a Multi-drone Routing Problem in Hazardous Waste Collection", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Youssef Harrath", "Jihene Kaabi", "Eman Alaradi", "Manar Alnoaimi", "Noor Alawadhi" ], "first_author": "Youssef Harrath", "venue": "International Journal of Advanced Computer Science and Applications", "publisher": "Science and Information Organization", "abstract": "This research investigates the problem of assigning pre-scheduled trips to multiple drones to collect hazardous waste from different sites in the minimum time. Each drone is subject to essential restrictions: maximum flying capacity and recharge operation. The goal is to assign the trips to the drones so that the waste is collected in the minimum time. This is done if the total flying time is equally distributed among the drones. An algorithm was developed to solve the problem. The algorithm is based on two main ideas: sort the trips according to a given priority rule and assign the current trip to the first available drone. Three different priority rules have been tested: Shortest Flying Time, Longest Flying Time, and Median Flying Time. Two recharging conditions are maintained: recharging needed time and recharging full duration. By applying each priority rule and each recharging condition, we generate a six versions of the algorithm. The six versions of the proposed algorithm were implemented in Java programming language.The results were analyzed and compared proving that the Longest Flying Time priority rule surpasses the other two rules. Moreover, recharging a drone just enough for taking the next trip proved to be better than fully recharging it.", "keywords": [ "Computer science", "Hazardous waste", "Drone", "Waste collection", "Vehicle routing problem", "Routing (electronic design automation)", "Routing algorithm", "Algorithm", "Operations research", "Municipal solid waste", "Computer network", "Routing protocol" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://thesai.org/Downloads/Volume15No2/Paper_98-An_Algorithm_Based_on_Priority_Rules.pdf" }, { "id": "https://openalex.org/W7160720559", "doi": "10.1016/j.eswa.2026.132596", "title": "Branch-and-price algorithm augmented by deep learning for the truck-drone routing problem with 3D loading constraints", "year": 2026, "publication_date": "2026-05-09", "type": "article", "language": "en", "authors": [ "Binjie Xu", "Guohua Wu", "Yalin Wang", "Qizhang Luo", "Conghui Wang", "Chenliang Liu", "Xinwei Wang" ], "first_author": "Binjie Xu", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Deep learning", "Routing (electronic design automation)", "Routing algorithm", "Optimization algorithm" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2026.132596" }, { "id": "https://openalex.org/W2898590705", "doi": "10.1002/net.21863", "title": "Preface: Special issue on Drone Delivery Systems", "year": 2018, "publication_date": "2018-10-24", "type": "editorial", "language": "en", "authors": [ "Niels A.H. Agatz", "James F. Campbell" ], "first_author": "Niels A.H. Agatz", "venue": "Networks", "publisher": "Wiley", "abstract": "Recent advances in autonomous vehicle technology create new opportunities to increase the efficiency and effectiveness of transportation systems.The use of drones (or unmanned aerial vehicles, UAVs) to support last-mile delivery operations is a topic receiving considerable attention.While there has been much research on the technical aspects of using autonomous drones, as well as significant financial investments in drone logistics systems, the operations research community has only recently started to focus on delivery drones.This special issue brings together state-of-the-art research related to the design and operations of drone delivery systems.It specifically focusses on optimization approaches (exact and heuristic) to support decision-making in these settings.We received 13 manuscripts, which were carefully reviewed through a rigorous refereeing process.Of the manuscripts submitted, six were accepted for publication in this special issue.In \"Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey,\" Otto et al. [5] provide an extensive literature survey on optimization approaches to civil applications of UAVs.The paper describes the most promising aerial drone applications and outlines characteristics of aerial drones relevant to operations planning.It reviews and classifies relevant literature across the key areas of drone operations and highlights novel modeling aspects related to drone use.Two papers consider a setting in which deliveries are performed by either a delivery vehicle or a drone, both departing from a common depot.The vehicles can serve multiple customers on a delivery tour, while the drones are constrained to perform back and forth trips.In \"An iterative two-step heuristic for the Parallel Drone Scheduling Traveling Salesman Problem,\" Saleu et al. [4] present a heuristic solution for the static version of this problem, where the objective is to minimize the completion time.Experiments conducted on benchmark instances show the efficiency of the approach.The paper \"Same-day delivery with a heterogeneous fleet of drones and vehicles\" by Ulmer and Thomas [6] focuses on the dynamic (same-day) version of the problem in which the objective is to maximize the expected number of served customers.To determine whether an order is delivered by a drone or by a vehicle, they present a policy function approximation based on geographical districting, which they show is highly effective.Two papers focus on a setting in which delivery drones are deployed from a regular delivery truck.The drone has to pick up a new parcel from the truck before each new delivery.The paper \"Drone delivery from trucks: Drone scheduling for given truck routes\" by Boysen et al. [2] considers various planning problems that arise in this setting with one or multiple drones.The authors provide an analysis of computational complexity and compare the potential benefits of different operational settings.In \"Dynamic programming approaches for the traveling salesman problem with drone,\" Bouman et al. [1] present exact solution approaches for the setting with one truck and one drone based on dynamic programming and provide an experimental comparison of these approaches.Their numerical experiments show that their solution approaches perform well and that restrictions on the number of locations the truck can visit while the drone is away can significantly reduce the solution times while having little impact on the overall solution quality.The paper \"Drone arc routing problems\" by Campbell et al.[3] presents several drone arc routing problems (Drone ARPs) and studies their relation with well-known postman arc routing problems.The ability of drones to travel directly between any two points in the plane without following the edges of the network leads to new problems and opportunities to reduce costs to serve a set of required edges.Novel problems are presented and solved using an algorithm for the rural postman problem.", "keywords": [ "Erasmus+", "Library science", "SAINT", "Operations research", "Computer science", "Art history", "Art", "Engineering", "The Renaissance" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/net.21863" }, { "id": "https://openalex.org/W4389711740", "doi": "10.1007/978-3-031-48751-4_17", "title": "Optimal Route for Drone for Monitoring of Crop Yields", "year": 2023, "publication_date": "2023-12-13", "type": "conference-paper", "language": "en", "authors": [ "Tatiana Makarovskikh", "Anatoly V. Panyukov", "Mostafa Salaheldin Abdelsalam Abotaleb", "В Ф Максимова", "Olga Dernova", "Eugeny Raschupkin" ], "first_author": "Tatiana Makarovskikh", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Operator (biology)", "Computer science", "Trajectory", "Task (project management)", "Routing (electronic design automation)", "Operations research", "Real-time computing", "Mathematical optimization", "Mathematics", "Algorithm" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-48751-4_17" }, { "id": "https://openalex.org/W4229031705", "doi": "10.1016/j.adhoc.2022.102878", "title": "LB-OPAR: Load balanced optimized predictive and adaptive routing for cooperative UAV networks", "year": 2022, "publication_date": "2022-05-05", "type": "article", "language": "en", "authors": [ "M. Gharib", "Fatemeh Afghah", "Elizabeth Serena Bentley" ], "first_author": "M. Gharib", "venue": "Ad Hoc Networks", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Benchmark (surveying)", "Routing (electronic design automation)", "Distributed computing", "Throughput", "Flow routing", "Destination-Sequenced Distance Vector routing", "Network performance", "Adaptive routing", "Static routing", "Computer network", "Link-state routing protocol" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.adhoc.2022.102878" }, { "id": "https://openalex.org/W3114377757", "doi": "10.20965/jaciii.2020.p0820", "title": "Three-Dimensional Unmanned Aerial Vehicle Route Planning Using Hybrid Differential Evolution", "year": 2020, "publication_date": "2020-12-19", "type": "article", "language": "en", "authors": [ "Hao Zhang", "Lihua Dou", "Chunxiao Cai", "Bin Xin" ], "first_author": "Hao Zhang", "venue": "Journal of Advanced Computational Intelligence and Intelligent Informatics", "publisher": "Fuji Technology Press Ltd.", "abstract": "Unmanned aerial vehicles (UAVs) have been investigated proactively owing to their promising applications. A route planner is key to UAV autonomous task execution. Herein, a hybrid differential evolution (HDE) algorithm is proposed to generate a high-quality and feasible route for fixed-wing UAVs in complex three-dimensional environments. A multiobjective function is designed, and both the route length and risk are optimized. Multiple constraints based on actual situations are considered, including UAV mobility, terrain, forbidden flying areas, and interference area constraints. Inspired by the wolf pack search algorithm, the proposed HDE algorithm combines differential evolution (DE) with an approaching strategy to improve the search capability. Moreover, considering the dynamic properties of fixed-wing UAVs, the quadratic B-spline curve is used for route smoothing. The HDE algorithm is compared with a state-of-the-art UAV route planning algorithm, i.e., the modified wolf pack search algorithm, and the traditional DE algorithm. Several numerical experiments are performed, and the performance comparison of algorithms shows that the HDE algorithm demonstrates better performances in terms of solution quality and constraint-handling ability in complex three-dimensional environments.", "keywords": [ "Computer science", "Differential evolution", "Algorithm", "Motion planning", "Smoothing", "Mathematical optimization", "Drone", "Constraint (computer-aided design)", "Terrain", "Artificial intelligence", "Robot", "Computer vision" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.20965/jaciii.2020.p0820" }, { "id": "https://openalex.org/W4280512241", "doi": "10.1117/12.2638778", "title": "Three-dimensional Euler solutions for drone delivery trajectory prediction under extreme environments", "year": 2022, "publication_date": "2022-05-17", "type": "conference-paper", "language": "en", "authors": [ "Shiven Hu", "Greg Mayer" ], "first_author": "Shiven Hu", "venue": "2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022)", "publisher": null, "abstract": "Unmanned aerial vehicles, or drones, offer the advantages to reduce the cost and time of delivering. However, efficient routing, especially in extreme conditions, is a prominent issue for unmanned delivery with drones and little work has been done to solve the routing problems. In this paper, we are going to consider the problem of the trajectory of drones under extreme weather because drone delivery typically faces with unexpected events such as wind from different directions which can affect the trajectory of the drone. The Euler method is used to do the numerical analysis to predict the trajectory. Numerical results show that the difference between the accurate solution and the numerical values is acceptable.", "keywords": [ "Drone", "Trajectory", "Computer science", "Routing (electronic design automation)", "Euler's formula", "Euler equations", "Aerospace engineering", "Mathematics", "Engineering", "Physics", "Computer network" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.2638778" }, { "id": "https://openalex.org/W4306773861", "doi": "10.3390/su142013459", "title": "Design and Assessment of an Urban Circular Combined Truck–Drone Delivery System Using Continuum Approximation Models and Integer Programming", "year": 2022, "publication_date": "2022-10-18", "type": "article", "language": "en", "authors": [ "David Canca", "Belén Navarro-Carmona", "José Luis Andrade-Pineda" ], "first_author": "David Canca", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The analysis of tandem truck–drone delivery systems has recently attracted the attention of the research community, mainly focused on extending classical operational research problems such as the multiple traveling salesperson or the vehicle-routing problem. In this paper, we explore the design of an urban massive combined delivery system using a continuum approximation (CA) method for a circular city characterized by a certain density of customers. Starting from a set of parameters defining the main characteristics of trucks and drones, a sectorization of the delivery area is first determined. Then, for a given truck capacity, the optimal number of trucks is obtained considering different scenarios using three integer programming models. We propose several performance indicators to compare the tandem approach with the alternative solely truck delivery system.", "keywords": [ "Truck", "Drone", "Integer programming", "Routing (electronic design automation)", "Computer science", "Mathematical optimization", "Vehicle routing problem", "Integer (computer science)", "Set (abstract data type)", "Delivery system", "Simulation", "Transport engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/14/20/13459/pdf?version=1666606217" }, { "id": "https://openalex.org/W4400921595", "doi": "10.1177/03611981241243076", "title": "Flying Sidekick Traveling Salesman Problem in Truck–Drone Team Logistics with Energy Issues", "year": 2024, "publication_date": "2024-07-23", "type": "article", "language": "en", "authors": [ "Beste Aksit", "Gökçe Özden", "İnci Sarıçiçek" ], "first_author": "Beste Aksit", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "Last-mile delivery has become increasingly important in recent years because of changes in customer needs. In this study, single-truck–multi-drone team logistics is considered. A mixed integer mathematical model is developed for the flying sidekick traveling salesman problem, a single-truck–multi-drone problem by considering technical and energy issues. Two different objective functions are used in the model. The first objective function is to minimize the sum of arrival times. Minimizing the sum of arrival times for customers also minimizes the distance traveled by the vehicles and, accordingly, the cost. The second objective function is to minimize total tardiness. A dataset is generated to test the proposed model. Trade-offs between the results for the two objective functions are analyzed. The technical requirements of the drone, energy limits, and fleet size are considered in the proposed mathematical model. The effects of the flight ranges of the drones are investigated with respect to the energy constraint. It has been observed that changes in the number of customers and drones affect the results of the objective functions. The total arrival time and tardiness decreased with increasing number of drones. Thus, the number of deliveries by truck decreases for customers. In addition, the advantages of this hybrid approach have been proven by comparing situations where only land vehicles are used with truck–drone team logistics.", "keywords": [ "Drone", "Travelling salesman problem", "Truck", "Tardiness", "Computer science", "Operations research", "Function (biology)", "Mathematical optimization", "Transport engineering", "Engineering", "Automotive engineering", "Mathematics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/03611981241243076" }, { "id": "https://openalex.org/W4411799049", "doi": "10.1109/access.2025.3584213", "title": "Distributionally Robust and Learning-Based Vehicle Routing for Disaster Blood Logistics", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Imane Hssini" ], "first_author": "Imane Hssini", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In humanitarian disasters, delivering blood and blood products rapidly and reliably is critical but logistically complex. This paper proposes a hybrid framework that integrates Distributionally Robust Optimization (DRO) for offline anticipative planning with Contextual Bandits for online adaptive vehicle dispatch. The system coordinates a heterogeneous fleet of ambulances, drones, and robots to navigate uncertain demand, infrastructure degradation, and time-sensitive deliveries. We formulate a two-level approach: a robust vehicle routing problem accounting for moment-based uncertainty in demand, and an online dispatch policy that learns context-aware allocation decisions in real time. Extensive simulations evaluate the proposed method against DRO-only, CB-only, and stochastic routing baselines. Results show that the hybrid approach outperforms all others in robustness, service rate, and delay, while maintaining reasonable computational efficiency.", "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Humanitarian Logistics", "Operations research", "Computer network", "Business", "Engineering", "Industrial organization" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2025.3584213" }, { "id": "https://openalex.org/W4410779314", "doi": "10.1049/itr2.70028", "title": "A MILP Model and Two‐Stage Heuristic Algorithm for Vehicle‐Assisted Multi‐Drone Inspection Routing Problem", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Yuanqing Xia", "He Luo", "Rui Dong", "Youlong Yin", "Shizhong Lin", "Guoqiang Wang" ], "first_author": "Yuanqing Xia", "venue": "IET Intelligent Transport Systems", "publisher": "Institution of Engineering and Technology", "abstract": "ABSTRACT This paper introduces a vehicle‐assisted multi‐drone inspection routing problem (VAMDIRP), which enables the vehicle to repeatedly traverse roads, thereby reducing task completion time. Firstly, a mixed‐integer linear programming (MILP) model is constructed for the problem using a series of decision variables and auxiliary variables. The model can be solved by GUROBI for small‐scale instances. Secondly, a two‐stage heuristic algorithm is proposed to solve large‐scale instances. Experimental results show that this algorithm improves the solution quality by an average of 20.71% and reduces the running time by an average of 60.31% compared to existing algorithms. Finally, a sensitivity analysis is conducted on relevant parameters, revealing that changes in the number and speed of drones significantly affect the solution quality.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Heuristic", "Routing (electronic design automation)", "Algorithm", "Meta heuristic", "Mathematical optimization", "Artificial intelligence", "Mathematics", "Embedded system" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1049/itr2.70028" }, { "id": "https://openalex.org/W3160572484", "doi": "10.22059/jieng.2021.320964.1756", "title": "A Multi-Visit Heterogeneous Drone Routing Model Considering Recharging Decision in Disaster", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Mehdi Changizi", "Donya Rahmani", "Reza Rmezanian" ], "first_author": "Mehdi Changizi", "venue": "Advances in Industrial Engineering", "publisher": null, "abstract": "The complex nature of disasters has required communities and governments to implement plans to reduce the disturbing effects of these disasters. With the breakdown and destruction of road infrastructure in times of disaster, the need to use an Unmanned Aerial Vehicle (UAV) fleet under the concept of humanitarian logistics has become increasingly essential. Therefore, we present a Multi-Visit Drone Routing Problem in this paper. The relief goods are delivered to the disaster-affected areas by using heterogeneous drones. We use a linear approximation function to calculate energy consumption. We formulated the proposed bi-objective Mixed Integer Linear Programming (MILP) model by a compromise programming method. To validate the proposed model and to show the model’s efficiency, we generate several test problems with the data extracted by experts. The computational results show the satisfactory performance of the model for the delivery of relief items to the damaged nodes by humanitarian drones in the shortest possible time.", "keywords": [ "Drone", "Integer programming", "Linear programming", "Routing (electronic design automation)", "Computer science", "Humanitarian Logistics", "Operations research", "Humanitarian aid", "Emergency management", "Search and rescue", "Vehicle routing problem", "Function (biology)" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://aie.ut.ac.ir/article_81141_0fe1d002a8890e8db2fa11ebca14ae3a.pdf" }, { "id": "https://openalex.org/W2541605069", "doi": "10.1109/dasc.2006.313771", "title": "Integration of Sensor Characteristics into Unmanned Aerial Vehicle Route (Re-) Planning", "year": 2006, "publication_date": "2006-10-01", "type": "conference-paper", "language": "en", "authors": [ "Steve Hart", "G.J.M. Koeners" ], "first_author": "Steve Hart", "venue": null, "publisher": null, "abstract": "In previous research into unmanned aerial vehicle (UAV) operator stations, functionality has been developed to support the navigator during mission execution (Tadema and Theunissen, 2003). It has been concluded that, due to the increase in requirements on the navigation plan inside and near the theater of operations (TOO), increased interaction between the UAV crew is required. The current research has focused on route planning based on sensor deployment, for situations where planning is a cooperative effort of geographically collocated and dispersed UAV operators. This paper presents two concepts to facilitate payload driven navigation planning inside and near the TOO. First, it introduces the capability of flight path mission requests to structure the UAV crew collaboration process. Secondly, it proposes to use spatially integrated depictions of navigation data regarding the sensor deployment to enhance the operator's situation awareness (SA). To demonstrate the possibilities of the concepts for a future UAV mission management system, the Delft University of Technology (TU Delft) avionics research group is extending a UAV mission management system research environment. A first step has been made by adding functionality to augment the UAV crew cooperation with flight path mission requests. Future extensions of functionality regard three-dimensional (3D) perspective display design to enhance the operators SA with respect to the sensor deployment and route planning", "keywords": [ "Software deployment", "Payload (computing)", "Avionics", "Crew", "Systems engineering", "Plan (archaeology)", "Computer science", "Motion planning", "Process (computing)", "Aeronautics", "Real-time computing", "Engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dasc.2006.313771" }, { "id": "https://openalex.org/W4391982436", "doi": "10.2139/ssrn.4732763", "title": "The Multi-Visit Drone-Assisted Routing Problem with Soft Time Windows and Stochastic Truck Travel Times", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Shan shan Meng", "Dong Li", "Ji yin Liu", "Yanru Chen" ], "first_author": "Shan shan Meng", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Truck", "Routing (electronic design automation)", "Computer science", "Operations research", "Business", "Engineering", "Computer network", "Automotive engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4732763" }, { "id": "https://openalex.org/W3197493961", "doi": "10.2197/ipsjtsldm.14.30", "title": "Energy-aware Routing of Delivery Drones under Windy Conditions", "year": 2021, "publication_date": "2021-01-01", "type": "article", "language": "en", "authors": [ "Satoshi Ito", "Hiroki Nishikawa", "Xiangbo Kong", "Yusuke Funabashi", "Atsuya Shibata", "Shunsuke Negoro", "Ittetsu Taniguchi", "Hiroyuki Tomiyama" ], "first_author": "Satoshi Ito", "venue": "IPSJ Transactions on System and LSI Design Methodology", "publisher": null, "abstract": "Drone is one of the promising vehicles that have exhibited the potential to reduce the cost and time in the field of logistics. However, due to the limitation of battery capacities, the flight time remains short. Therefore, energy consumption is one of the most critical concerns in drone delivery services. In order to reduce the energy consumption, drone generally needs to fly to the destination in as short a time as possible. For delivery services, the drone has loads to deliver and is exposed to weather effects such as windy conditions. This paper studies a routing problem for energy minimization of delivery drones under the assumption of windy conditions. This paper formally defines Energy Minimizing Vehicle Routing Problem (EMVRP) under windy conditions. Experimental scenarios with different wind velocities and the number of customers have been simulated, and demonstrate a comparison of the metrics in the energy consumption and the flight distance.", "keywords": [ "Drone", "Computer science", "Energy consumption", "Routing (electronic design automation)", "Energy (signal processing)", "Minification", "Real-time computing", "Computer network", "Electrical engineering", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.jstage.jst.go.jp/article/ipsjtsldm/14/0/14_30/_pdf" }, { "id": "https://openalex.org/W4402619587", "doi": "10.1016/j.engappai.2024.109337", "title": "A novel solution for routing a swarm of drones operated on a mobile host", "year": 2024, "publication_date": "2024-09-19", "type": "article", "language": "en", "authors": [ "Halil Savuran", "Murat Karakaya" ], "first_author": "Halil Savuran", "venue": "Engineering Applications of Artificial Intelligence", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Host (biology)", "Drone", "Swarm behaviour", "Routing (electronic design automation)", "Computer network", "Distributed computing", "Artificial intelligence", "Ecology", "Biology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.engappai.2024.109337" }, { "id": "https://openalex.org/W4312422883", "doi": "10.2139/ssrn.4271364", "title": "Drone Fleet Deployment Optimization for Direct Delivery with Time Windows and Battery Replacements", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Tanveer Hossain Bhuiyan", "Mohammad Roni", "Victor Walker" ], "first_author": "Tanveer Hossain Bhuiyan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Software deployment", "Battery (electricity)", "Drone", "Computer science", "Automotive engineering", "Engineering", "Aeronautics", "Operating system", "Physics", "Biology" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4271364" }, { "id": "https://openalex.org/W2989695201", "doi": "10.1109/icves.2019.8906323", "title": "Mobility and Energy Aware Data Routing for UAV-Assisted VANETs", "year": 2019, "publication_date": "2019-09-01", "type": "conference-paper", "language": "en", "authors": [ "Hakim Ghazzai", "Ahmed K. F. Khattab", "Yehia Massoud" ], "first_author": "Hakim Ghazzai", "venue": null, "publisher": null, "abstract": "In this paper, we develop a mobility and energy aware data routing protocol for unmanned aerial vehicle-assisted vehicular ad-hoc networks (UAV-assisted VANETs). One of the UAV act as a flying roadside Uunit (RSU) collecting data from ground vehicles, while the other UAVs play the role of relays to deliver the data to mobility service center (MSC). The UAVs can adjust their three-dimensional (3D) locations within a predefined range, if needed, in order to ensure reliable communication links. The proposed approach aims to minimize the energy consumed by the UAVs in both data transfer and movement. which ensures fair distribution of the routing effort across the different UAVs in the network. This is achieved by taking the residual UAV energy into account in the routing decision. We formulate such a routing problem as a mixed integer non-linear program (MINLP) to determine both the selected route and the locations of the UAVs participating in the data transfer process. Since such a problem is non-convex, we proceed with a joint optimization solution where the route is optimized using an ILP and the UAVs’ 3D locations are determined using the meta-heuristic particle swarm optimization (PSO) algorithm. We present a selected set of numerical results to illustrate the performance of the proposed solution for different scenarios and compare it to a meta-heuristic approach based on swarm intelligence.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Heuristic", "Particle swarm optimization", "Routing protocol", "Computer network", "Destination-Sequenced Distance Vector routing", "Wireless ad hoc network", "Distributed computing", "Real-time computing", "Link-state routing protocol", "Algorithm" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icves.2019.8906323" }, { "id": "https://openalex.org/W4401387122", "doi": "10.1109/jiot.2024.3440017", "title": "DAWN: Dynamic Task Planning of Multi-UAV With Two-Layer Optimization Mechanism in Uncertain Environments", "year": 2024, "publication_date": "2024-08-07", "type": "article", "language": "en", "authors": [ "Daqian Liu", "Bowen Fei", "Weidong Bao", "Xiaomin Zhu", "Xiaoqing Li" ], "first_author": "Daqian Liu", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "UAV cooperative formation provides rescue and material delivery for the industrial Internet of Things (IIoT). To solve issues, such as low material distribution efficiency and poor mobility during disaster rescue, we propose a two-layer optimization mechanism-based multiple UAV dynamic task planning method (DAWN), which can cope with the problem of the global communication link unreachable caused by disasters. Specifically, we consider the global task allocation as a dynamic vehicle routing problem (VRP) and use deep reinforcement learning (DRL) to solve it so as to minimize the global flight path and energy consumption. Second, based on the current communication structure, we establish a local path planning approach based on the trust network that maximizes the regional coverage rate while minimizing the flight paths. On the basis of these two layers, an UAV formation dynamic task planning approach is realized. Experimental results prove that the proposed DAWN can obtain the optimal flight paths and achieve higher energy efficiency while providing reasonable region coverage to discover more potential tasks.", "keywords": [ "Computer science", "Mechanism (biology)", "Task (project management)", "Layer (electronics)", "Distributed computing", "Real-time computing", "Systems engineering", "Engineering" ], "cited_by_count": 22, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2024.3440017" }, { "id": "https://openalex.org/W7131619759", "doi": "10.1016/j.trc.2026.105594", "title": "Slope-dependent energy optimization for cooperative truck-drone delivery and pickup routing with time windows", "year": 2026, "publication_date": "2026-02-26", "type": "article", "language": "en", "authors": [ "Ying Wang", "Taofeng Ye", "Jiajun Sun", "Jicong Duan" ], "first_author": "Ying Wang", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Energy consumption", "Pickup", "Benchmark (surveying)", "Vehicle routing problem", "Scalability", "Schedule", "Terrain", "Routing (electronic design automation)", "Fuel efficiency" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2026.105594" }, { "id": "https://openalex.org/W3090258086", "doi": "10.1007/978-3-030-67369-7_5", "title": "Joint Mobility-Aware UAV Placement and Routing in Multi-Hop UAV Relaying Systems", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Anousheh Gholami", "Nariman Torkzaban", "John S. Baras", "Chrysa Papagianni" ], "first_author": "Anousheh Gholami", "venue": "Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering", "publisher": null, "abstract": null, "keywords": [ "Computer science", "Computer network", "Exploit", "Software deployment", "Wireless network", "Hop (telecommunications)", "Base station", "Wireless", "Distributed computing", "Telecommunications", "Computer security" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2009.14446" }, { "id": "https://openalex.org/W2889031481", "doi": "10.1007/978-3-319-99996-8_21", "title": "Factors Affecting Energy Consumption of Unmanned Aerial Vehicles: An Analysis of How Energy Consumption Changes in Relation to UAV Routing", "year": 2018, "publication_date": "2018-08-27", "type": "conference-paper", "language": "en", "authors": [ "Amila Thibbotuwawa", "Peter Nielsen", "Zbigniew Antoni Banaszak", "Grzegorz Bocewicz" ], "first_author": "Amila Thibbotuwawa", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Energy consumption", "Routing (electronic design automation)", "Computer science", "Consumption (sociology)", "Relation (database)", "Energy (signal processing)", "Computer network", "Engineering", "Data mining", "Mathematics", "Statistics" ], "cited_by_count": 34, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-319-99996-8_21" }, { "id": "https://openalex.org/W4408195036", "doi": "10.26650/jtl.2025.1609155", "title": "Two-Echelon Location Selection and Unmanned Aerial Vehicle Assisted Vehicle Routing Problem", "year": 2025, "publication_date": "2025-03-06", "type": "article", "language": "en", "authors": [ "Tolga Gedikli", "İsmail Karaoğlan" ], "first_author": "Tolga Gedikli", "venue": "Journal of Transportation and Logistics", "publisher": "Istanbul University", "abstract": "In recent years, the potential use of Unmanned Aerial Vehicle (UAV) technology in logistics processes and the rapid developments in this field have made UAV-assisted routing problems an important research topic. In this paper, we consider a two-echelon location selection and UAV-assisted vehicle routing problem. In this problem, trucks leave from the main depot with their packages and UAVs, move through potential service points (mobile depot), and return to the main depot. At each mobile depot, UAVs are launched from the truck to service the customers, and the trucks wait for the UAVs to complete their service and return. The UAVs are capable of making multiple trips between the mobile depot and customers, carrying only one package at a time. The objective is to minimize the maximum tour time. In this paper, a new problem, which involves a multi-truck structure and multiple launches of UAVs from a mobile depot, is investigated for the first time to the best of our knowledge in the literature. A mixed integer linear programming model is proposed to solve the problem. In order to evaluate the performance of the proposed mathematical model, the number of trucks is determined as one and two, experimental analyses are performed and the results are reported in detail.", "keywords": [ "Vehicle routing problem", "Selection (genetic algorithm)", "Computer science", "Routing (electronic design automation)", "Operations research", "Aeronautics", "Artificial intelligence", "Engineering", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.26650/jtl.2025.1609155" }, { "id": "https://openalex.org/W3080471", "doi": "10.1007/978-90-481-9707-1_14", "title": "Cooperative Mission and Path Planning for a Team of UAVs", "year": 2014, "publication_date": "2014-08-09", "type": "book-chapter", "language": "en", "authors": [ "Hyondong Oh", "Hyo‐Sang Shin", "Seungkeun Kim", "Antonios Tsourdos", "Brian A. White" ], "first_author": "Hyondong Oh", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Motion planning", "Computer science", "Path (computing)", "Context (archaeology)", "Patrolling", "Shortest path problem", "Mathematical optimization", "Routing (electronic design automation)", "Relay", "Distributed computing", "Robot", "Computer network" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-90-481-9707-1_14" }, { "id": "https://openalex.org/W2549924565", "doi": "10.1109/control.2016.7737644", "title": "Intelligent foresight for UAV routing problems", "year": 2016, "publication_date": "2016-08-01", "type": "conference-paper", "language": "en", "authors": [ "Theopisti Zaza", "Arthur Richards" ], "first_author": "Theopisti Zaza", "venue": null, "publisher": null, "abstract": "This paper describes an Intelligent Foresight (IF) method for improving the response time of a UAV routing optimisation, using the optimal solution of a smaller size scenario, as a warm-start for the Ant Colony Optimisation. The method aims to predict the revised routing and allocation of UAVs in the event of a new task arising. The new task location is not known: solutions are calculated for a range of possible locations. Therefore, a smart way of sampling and tessellating the plane is introduced to distinguish the areas where the solution will not change if that task is added. This hybrid optimisation was created to offer near-optimal solutions, under the condition that its computational time would be less than an exact method would require to solve only one scenario. The IF method was tested for a variety of scenarios and benchmarked against the Gurobi software. The results showed that the IF offers a good approximation of how the solution will change with its computational time remaining approximately the same, regardless the size or the complexity of the scenarios solved.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Task (project management)", "Futures studies", "Variety (cybernetics)", "Range (aeronautics)", "Event (particle physics)", "Mathematical optimization", "Ant colony optimization algorithms", "Artificial intelligence", "Engineering", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/control.2016.7737644" }, { "id": "https://openalex.org/W4416003777", "doi": "10.3390/wevj16110612", "title": "Adaptive Large Neighborhood Search for a Flexible Truck–Drone Routing Problem with Multi-Visit and Cost Trade-Offs", "year": 2025, "publication_date": "2025-11-07", "type": "article", "language": "en", "authors": [ "Jiang Bian", "Rui Zhou", "Qingbin Meng" ], "first_author": "Jiang Bian", "venue": "World Electric Vehicle Journal", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Extending the truck–drone mothership system, this paper addresses a flexible multi-visit truck and drone joint routing problem (FMTDJRP). A mixed integer linear program is proposed to minimize total travel cost. An adaptive large neighborhood search with a knowledge-based acceleration strategy yields near-optimal solutions. Experiments across varied customer distributions, drone specs, and truck-–drone cost ratios confirm flexibility, adaptability, and cost efficiency.", "keywords": [ "Drone", "Routing (electronic design automation)", "Integer programming", "Vehicle routing problem", "Truck", "Linear programming", "Acceleration", "Joint (building)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2032-6653/16/11/612/pdf?version=1762517089" }, { "id": "https://openalex.org/W7117255856", "doi": "10.1109/tits.2025.3646969", "title": "A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring", "year": 2025, "publication_date": "2025-12-25", "type": "article", "language": null, "authors": [ "Yumeng Bai", "Yiheng Feng" ], "first_author": "Yumeng Bai", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Unmanned Aerial Vehicles (UAVs) have great potential in urban traffic monitoring. However, their limited flight duration presents critical challenges in sustainable recharging strategies and efficient route planning in long-term monitoring tasks. Additionally, existing approaches for long-term monitoring often neglect the dynamic and evolving nature of urban traffic networks. In this study, we introduce a novel dynamic UAV routing framework for long-term, network-wide urban traffic monitoring, leveraging existing ground vehicles (GVs), such as public transit buses, as mobile charging stations without disrupting their operations. To address the complexity of long-term monitoring scenarios involving multiple flights, we decompose the problem into manageable single-flight tasks, in which each flight is modeled as a Team Arc Orienteering Problem with Decreasing Profits (TAOP-DP) with the objective to collectively maximize the spatiotemporal network coverage. Between flights, we adaptively update the edge weights to incorporate real-time traffic changes and revisit intervals. We validate our framework through extensive microscopic simulations in a modified Sioux Falls network under various scenarios, including static and changing demand levels and varying historical information availability. Comparative results demonstrate that our model outperforms three baseline approaches, especially when historical information is incomplete or absent. The scalability and generalizability of the framework are further validated through a portion of the Manhattan network with real world probe vehicle data. Moreover, we show that the proposed monitoring framework can capture network-wide traffic trends and construct accurate Macroscopic Fundamental Diagrams (MFDs), which highlights its potential in network-wide traffic control and management applications.", "keywords": [ "Scalability", "Routing (electronic design automation)", "Benchmark (surveying)", "Enhanced Data Rates for GSM Evolution", "Baseline (sea)", "Traffic flow (computer networking)", "Public transport", "Resilience (materials science)", "Traffic generation model" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2025.3646969" }, { "id": "https://openalex.org/W4412947456", "doi": "10.26599/tst.2024.9010219", "title": "DQN-ALNS for UAV Reconnaissance Routing Problem with Target Priority Consideration", "year": 2025, "publication_date": "2025-08-05", "type": "article", "language": "en", "authors": [ "Lingjie Zhou", "Zhihao Luo", "Jianmai Shi" ], "first_author": "Lingjie Zhou", "venue": "Tsinghua Science & Technology", "publisher": "Tsinghua University Press", "abstract": "In recent years, UAVs have been extensively employed for reconnaissance missions. Our focus is prioritizing reconnaissance of high-priority targets while minimizing the flight duration when UAV power is constrained. We introduce a framework called DQN-ALNS, which integrates Deep Q Network (DQN) into the Adaptive Large Neighborhood Search (ALNS) metaheuristic algorithm to optimize the process through the current solution’s search state. Specifically, the agent is utilized to select the destroy-repair operators to update a new solution, thereby iteratively optimizing the UAV reconnaissance routes. Experimental results reveal that DQN-ALNS achieves superior solutions and faster convergence than other comparison algorithms. The algorithm leverages the exploratory potential of the current solution and demonstrates robust stability. The final sensitivity analysis showcases that reconnaissance missions with high priority are better accomplished when the UAV power is moderate and the target priority is concentrated at smaller values.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Operations research", "Computer network", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.26599/tst.2024.9010219" }, { "id": "https://openalex.org/W4407012431", "doi": "10.48550/arxiv.2501.18432", "title": "Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigms", "year": 2025, "publication_date": "2025-01-30", "type": "preprint", "language": "en", "authors": [ "Eneko Osaba", "Pablo Miranda-Rodriguez", "Andreas Oikonomakis", "Matic Petrič", "Ruiz, Alejandra", "Sebastian Böck", "Michail‐Alexandros Kourtis" ], "first_author": "Eneko Osaba", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System's devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.", "keywords": [ "Quantum annealing", "Drone", "Quantum computer", "Computer science", "Simulated annealing", "Routing (electronic design automation)", "Quantum", "Distributed computing", "Theoretical computer science", "Computer network", "Algorithm", "Physics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2501.18432" }, { "id": "https://openalex.org/W4399169361", "doi": "10.1109/wf-iot58464.2023.10539596", "title": "3D Drone Path Planning Algorithms with Collision Avoidance: Collision Resolution vs. Prevention", "year": 2023, "publication_date": "2023-10-12", "type": "conference-paper", "language": "en", "authors": [ "Rutuja Shivgan", "Jorge H. Medina", "Ziqian Dong", "Roberto Rojas‐Cessa" ], "first_author": "Rutuja Shivgan", "venue": null, "publisher": null, "abstract": "Because of their limited flight range, multiple drones are often deployed simultaneously to perform complex tasks. The flight path is planned for each drone to follow to complete the job before task execution. However, multi-drone path planning places drones at risk of in-flight collisions. To overcome this problem, we model the multi-drone path planning problem as a multi-vehicle routing problem that maximizes job coverage subject to collision-free paths. We propose three 3D collision-free path planning algorithms, namely, XTRACT, 3DETACH, and ASCEND. XTRACT and 3DETACH provide collision-free paths by setting partial paths at different altitudes while ASCEND prevents intersecting paths at the planning phase by selecting different altitudes. We limit the search processes to two altitudes to demonstrate sufficiency with the lowest height complexity. Through exhaustive evaluations, we compare the performance of the proposed schemes and show the trade-offs between resolving and preventing collisions from path planning. We identify the best-performing strategy by using a profit model to evaluate the plethora of applicable performance metrics.", "keywords": [ "Drone", "Motion planning", "Computer science", "Collision", "Collision avoidance", "Path (computing)", "Collision detection", "Any-angle path planning", "Real-time computing", "Algorithm", "Mathematical optimization", "Simulation" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/wf-iot58464.2023.10539596" }, { "id": "https://openalex.org/W4376113018", "doi": "10.46254/ap03.20220518", "title": "A Comparison of Ant Colony Optimization and Depth First Search for Solving Unmanned Aerial Vehicle – Ground Vehicle Routing Problem in Humanitarian Logistics", "year": 2023, "publication_date": "2023-05-10", "type": "conference-paper", "language": "en", "authors": [ "Alfredo Aryasena", "Bertha Maya Sopha" ], "first_author": "Alfredo Aryasena", "venue": null, "publisher": null, "abstract": "Time is of the essence in disaster relief operation.The casualties might be huge if disaster relief operation can't be done quickly.The broken infrastructure and post-disaster environment make it difficult to search for survivors.To solve this, Unmanned Aerial Vehicle (UAV) is deployed since UAV can survey the location easily from the air.One downside of UAV is its limited energy capacity.UAV needs to be deployed with a Ground Vehicle (GV) who charges or swaps UAV battery during the operation.As such, the coordination between UAV and GV becomes an important part in the operation.This research focuses on constructing a route for the UAV -GV to find survivors using Ant Colony Optimization (ACO).The studied case is the 2010 Merapi eruption, which is one of the volcano eruptions in Indonesia.The result of this research will be compared with the result from previous research that used Depth-First Search (DFS).The route from ACO needs 90.48 minutes to complete whereas the route from previous research needs 95.48 minutes, implying that the route evaluated using ACO results in 5 minutes faster than that constructed using DFS.", "keywords": [ "Ant colony optimization algorithms", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Unmanned ground vehicle", "ANT", "Artificial intelligence", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://ieomsociety.org/proceedings/2022malaysia/518.pdf" }, { "id": "https://openalex.org/W4415766928", "doi": "10.1016/j.cor.2025.107324", "title": "An enhanced dual-population NSGA-II for solving multi-objective emergency supplies dispatch problem based on truck-drone-dispatch vehicle collaboration", "year": 2025, "publication_date": "2025-11-01", "type": "article", "language": "en", "authors": [ "Qin Guo", "Ya Qiu", "Yong Shi", "Qiaoxian Zheng", "Haixiang Guo" ], "first_author": "Qin Guo", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Sorting", "Genetic algorithm", "Population", "Key (lock)", "Pareto principle", "Multi-objective optimization", "Convergence (economics)", "Truck" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2025.107324" }, { "id": "https://openalex.org/W4411406739", "doi": "10.1109/jiot.2025.3578613", "title": "Energy-Optimized Path Planning for UAVs to Minimize Fleet Size in Time-Sensitive Data Collection Tasks", "year": 2025, "publication_date": "2025-06-18", "type": "article", "language": "en", "authors": [ "Yang Yu", "Sanghwan Lee" ], "first_author": "Yang Yu", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In this paper, we investigate the problem of completing data collection tasks for IoTDs (Internet of Things devices) using Unmanned Aerial Vehicles (UAVs) under the constraints of UAVbattery capacity and predefined data age. Our objective was to minimize UAV’s flight energy consumption by optimizing their trajectory allocation and achieve the research goal with the minimum number of UAVs. We transformed the multi-objective optimization problem (MOP) into a Traveling Salesman Problem with Neighborhoods (TSPN) by considering UAVs’ ability to collect data within the communication range of IoT devices. We studied two modes of data collection for UAVs: one in which they can only collect data during hovering and another in which they can collect data while hovering and moving simultaneously. We proposed two algorithms: UAV hovering data collection algorithm with neighborhood (UHDCN) and UAV moving data collection algorithm with neighborhood (UMDCN). We evaluate the performance of the proposed algorithms through extensive simulation experiments. The results demonstrate that UHDCN algorithm requires at least 4.8% fewer number of UAVs compared to existing algorithms, while UMDCN algorithm requires at least 22.2% fewer number of UAVs. Additionally, UHDCN algorithm consumes at least 4.9% less total energy compared to existing algorithms, while UMDCN algorithm consumes at least 22.8 less energy.", "keywords": [ "Computer science", "Data collection", "Motion planning", "Real-time computing", "Energy (signal processing)", "Path (computing)", "Simulation", "Computer network", "Artificial intelligence", "Robot" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2025.3578613" }, { "id": "https://openalex.org/W2439402276", "doi": "10.1109/siu.2016.7496013", "title": "UAV path planning with multiagent Ant Colony system approach", "year": 2016, "publication_date": "2016-05-01", "type": "conference-paper", "language": "en", "authors": [ "Seda KAZDAL ÇALIK" ], "first_author": "Seda KAZDAL ÇALIK", "venue": null, "publisher": null, "abstract": "In this study, a solution approach of the problem of globally optimal trajectory planning of Unmanned Aerial Vehicle (UAV) has been proposed modeling as Ant Colony Optimization (ACO) with a multiagent structure. The solution of UAV's route problem in environments that have a large number of control points and obstacles such as radars and mountain is very complex and can be solved with the help of the different optimization algorithms. The proposed method is modeled on Netlogo, an agent-based programming environment. Simulation results show that the ACO greatly optimize the route length and reduce the average flight time for UAV's route planning.", "keywords": [ "NetLogo", "Ant colony optimization algorithms", "Computer science", "Motion planning", "Route planning", "Ant colony", "Multi-agent system", "Trajectory", "Path (computing)", "Real-time computing", "Mathematical optimization", "Artificial intelligence" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/siu.2016.7496013" }, { "id": "https://openalex.org/W2606162914", "doi": null, "title": "Mission Planning for Sensor Network Deployment using a Fleet of Drones", "year": 2016, "publication_date": "2016-01-01", "type": "dissertation", "language": "en", "authors": [ "Purva Goyal" ], "first_author": "Purva Goyal", "venue": "Research Repository (Delft University of Technology)", "publisher": "Delft University of Technology", "abstract": "Various methods for route planning of on-road vehicles to serve transportation requests have been developed in the literature in order to reduce transportation and operational costs. The applicability and thus development of these methods is primarily motivated by the field of application. This article deals with the mission planning for a fleet of drones to deploy sensors in a network. In particular, they are conceived to complete the task of delivering geophones in the seismic surveys. Unlike conventional on-road vehicles used for delivery purposes, every drone in the fleet is constrained to make a frequent return trip back to the depot to pick-up a new payload and restore its battery. A centralized planner is proposed in this article due to this constraint. The problem of planning is decomposed into two phases: route formation and route scheduling. The first phase is handled using the extensive formulation of Multi-Trip Vehicle Routing Problem (MTVRP) aiming at minimizing the overall journey time. A heuristic method is also proposed for this phase which provides near-optimal solutions in a computationally efficient manner. The second phase of the planning algorithm deals with the unaddressed problem of depot congestion arising due to the frequent visits of each drone to the depot. This problem is expressed in the form of a Mixed-Integer Linear Program (MILP) that can be solved using available software. This phase is computationally intensive and comparatively slow which restricts the usage of this mission planner in the re-planning phase to the cases involving longer journeys with limited number of routes. The results from a flight-test are also presented in order to demonstrate the mission planner.", "keywords": [ "Planner", "Computer science", "Drone", "Software deployment", "Scheduling (production processes)", "Integer programming", "Operations research", "Heuristic", "Linear programming", "Real-time computing", "Engineering", "Operations management" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://resolver.tudelft.nl/uuid:e5604e9a-c241-4236-83dd-5fc823e7e284" }, { "id": "https://openalex.org/W4308160341", "doi": "10.1007/s11036-022-02045-y", "title": "Multirole UAVs Supported Parking Surveillance System", "year": 2022, "publication_date": "2022-11-02", "type": "article", "language": "en", "authors": [ "Goran Jauševac", "Dalibor Dobrilović", "Vladimir Brtka", "Gordana Jotanović", "Dragan Peraković", "Željko Stojanov" ], "first_author": "Goran Jauševac", "venue": "Mobile Networks and Applications", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Set (abstract data type)", "Convolutional neural network", "Routing (electronic design automation)", "Process (computing)", "Real-time computing", "Artificial intelligence", "Travelling salesman problem", "Test set", "Embedded system", "Algorithm" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s11036-022-02045-y" }, { "id": "https://openalex.org/W3217515368", "doi": "10.1142/s2301385022500170", "title": "Multi-UAV Coverage Path Planning for Gas Distribution Map Applications", "year": 2021, "publication_date": "2021-11-19", "type": "article", "language": "en", "authors": [ "Abdelwahhab Bouras", "Yasser Bouzid", "Mohamed Guiatni" ], "first_author": "Abdelwahhab Bouras", "venue": "Unmanned Systems", "publisher": "World Scientific", "abstract": "Aerial coverage for Gas Distribution Map (GDM) using Unmanned Aerial Vehicles (UAVs) with olfaction capabilities has become a key for effectiveness and cost improvements. In this work, the main contribution is the application of Coverage Path Planning (CPP) approaches adapted to survey gas plumes using an autonomous fleet of UAVs. Indeed, the mission is carried out in four steps. First, a gas plume model was integrated to simulate the environment. Then, based on the sensor’s footprint model and the Voronoï diagram, an exact cellular decomposition method is proposed to generate and distribute measurement points or Points Of Interests (POIs) effectively. To take measurements from all these POIs, two planning approaches were proposed in the third step. One is based on the separation of missions between UAVs using the K-means algorithm principle and modeling each path as a Traveling Salesman Problem (TSP). While the second approach models the global mission for all UAVs as a Vehicle Routing Problem (VRP). The resolution of the two mathematical models is achieved using Genetic Algorithms (GAs) and Simulated Annealing (SA) algorithms, respectively. Eventually, the GDM is reconstituted based on triangulation-based cubic interpolation. The evaluation of the proposed simulation techniques based on comparative analyses shows their efficiency, speed and flexibility. The proposed methods take into account the UAV’s flight autonomy and the desired coverage quality, and they provide solutions for a variety of coverage applications, particularly for GDM.", "keywords": [ "Computer science", "Simulated annealing", "Motion planning", "Travelling salesman problem", "Flexibility (engineering)", "Mathematical optimization", "Vehicle routing problem", "Real-time computing", "Routing (electronic design automation)", "Algorithm", "Artificial intelligence", "Mathematics" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s2301385022500170" }, { "id": "https://openalex.org/W4399039506", "doi": "10.1109/tvt.2024.3405733", "title": "Optimized Routing Protocol Through Exploitation of Trajectory Knowledge for UAV Swarms", "year": 2024, "publication_date": "2024-05-27", "type": "article", "language": "en", "authors": [ "Zihao Zhou", "Jie Tang", "Wanmei Feng", "Nan Zhao", "Zhutian Yang", "Kai‐Kit Wong" ], "first_author": "Zihao Zhou", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Due to its strong flexibility, easy deployment and extensive connectivity, unmanned aerial vehicle (UAV) swarm has been widely used in emergency communication in recent years, especially in the case when terrestrial communication infrastructures are no longer available. Within a UAV swarm, the design of routing protocol is one of the most challenging problems that enables cooperation among multiple UAVs to perform complex tasks in disaster areas. Nevertheless, the routing protocols reported so far have made the simplifying assumption that the UAV nodes move randomly. In the context of mission-oriented scenarios, such as emergency communication in disaster areas, this assumption appears evidently impractical. On the contrary, for many UAV applications, the trajectories of UAVs are pre-planned in advance through mission planning and trajectory planning. Disregarding trajectory information from the application layer may result in routing protocols struggling to adapt to rapid changes in network topology and facing challenges in achieving optimal performance across various communication metrics. To break the bottleneck, in this paper, we propose an efficient trajectory-based optimized routing protocol (TORP) for mission-oriented UAV swarm. Firstly, we model the packet routing problem using the concept of time-dependent graph. The objective is to maximize network performance, considering the power consumption and end-to-end (E2E) delay. The formulated problem is a binary linear programming (BLP) problem which is intractable to solve. Firstly, we propose a modified dynamically weighted Dijkstra's algorithm (MDWD). Based on the MDWD algorithm, we further propose an efficient optimization scheme to solve the constructed optimization problem. The simulation results demonstrate that our algorithm can make effective routing selections in dynamic UAV network, thereby improving the system performance in terms of packet delivery ratio, end-toend delay and power consumption.", "keywords": [ "Trajectory", "Routing protocol", "Computer science", "Computer network", "Routing (electronic design automation)", "Protocol (science)", "Zone Routing Protocol", "Engineering", "Wireless Routing Protocol", "Physics" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2024.3405733" }, { "id": "https://openalex.org/W2905159397", "doi": "10.1109/smartworld.2018.00295", "title": "Joint Computation Offloading and Routing Optimization for UAV-Edge-Cloud Computing Environments", "year": 2018, "publication_date": "2018-10-01", "type": "conference-paper", "language": "en", "authors": [ "Baichuan Liu", "Huawei Huang", "Song Guo", "Wuhui Chen", "Zibin Zheng" ], "first_author": "Baichuan Liu", "venue": null, "publisher": null, "abstract": "Computation offloading is significant to the UAV swarms by migrating computational tasks from the UAV swarms to the edge or cloud computing infrastructure. However, when studying the computation offloading problem, existing approaches do not fully consider the characteristics of UAV swarms and do not differentiate the characteristics of cloud computing and edge computing well. In this paper, we study a joint computation offloading and routing optimization problem for UAV swarms under an UAV-Edge-Cloud computing architecture. Our contributions can be summarized as the following three aspects. First, to fully optimize the characteristics of UAV swarms, we jointly consider the computation offloading and routing for UAV swarms. Then, to highlight the characteristics of cloud computing and edge computing, we propose a novel three-layer computing model joint computation offloading and routing problem. Finally, we design a polynomial near-optimal approximation algorithm to solve the joint optimization problem using the Markov approximation technique. Finally, our simulation results demonstrate the high efficiency of our proposed algorithm.", "keywords": [ "Computation offloading", "Computer science", "Cloud computing", "Edge computing", "Computation", "Distributed computing", "Routing (electronic design automation)", "Enhanced Data Rates for GSM Evolution", "Mobile edge computing", "Optimization problem", "Computer network", "Algorithm" ], "cited_by_count": 22, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/smartworld.2018.00295" }, { "id": "https://openalex.org/W2968343056", "doi": null, "title": "Introduction of drones in the last-mile logistic process of medical product delivery: A feasibility assessment applied to the case study of Benu 't Slag", "year": 2019, "publication_date": "2019-01-01", "type": "dissertation", "language": "en", "authors": [ "Irene Zubin" ], "first_author": "Irene Zubin", "venue": "Research Repository (Delft University of Technology)", "publisher": "Delft University of Technology", "abstract": "The term last-mile delivery refers to the final leg of a business-to-customer service, in which a product is shipped from a depot to a final destination point by means of land transportation, such as vans and small trucks. Although they provide a common and easy way to consign products, companies are striving for new transport technologies to reduce congestion problems, infrastructure limitations and air pollution. Recently, a valid alternative to road-bounded vehicles that is has gained attention is the adoption of drones in the delivery fleet. Drone applications range from military training, surveillance, path recognition and shipment of perishable products in emergency situations. Research on drones as delivery vehicles is still on its early stages, with some practical trials carried out by leader companies such as Google and Amazon. However, the application of drones in the pharmaceutical sector, and specifically for home deliveries of medical products, has not been investigated yet. To gain new insights into the feasibility of introducing drones in the delivery fleet composition, drone applications were studied for the delivery operations of the pharmacy BENU t Slag, in Rotterdam. Two scenario alternatives were tested using the Vehicle Routing Problem (VRP) formulation and a Large- scale Neighbourhood Search (LNS) algorithm was implemented to estimate the performance indicators associated with each scenario. Performances were then analysed through a comparative analysis. Conclusively, indicators were found to improve the delivery performance when drones are included in the fleet composition, with gains in environmental aspects, service time and delivery costs. Results provide important information for further research on the implications of using drones in the pharmaceutical sector. Moreover, results are also useful for BENU t Slag, providing a valid feasibility assessment of using drones for their home delivery operations.", "keywords": [ "Drone", "Truck", "Transport engineering", "Service (business)", "Last mile (transportation)", "Product (mathematics)", "Operations research", "Business", "Computer science", "Operations management", "Engineering", "Marketing" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://resolver.tudelft.nl/uuid:0db692ce-5685-4499-bf84-d570544de795" }, { "id": "https://openalex.org/W3006967407", "doi": "10.1007/978-3-030-40971-5_11", "title": "A Proactive Approach to Resistant UAV Mission Planning", "year": 2020, "publication_date": "2020-01-01", "type": "conference-paper", "language": "en", "authors": [ "Grzegorz Radzki", "Peter Nielsen", "Grzegorz Bocewicz", "Zbigniew Antoni Banaszak" ], "first_author": "Grzegorz Radzki", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Knapsack problem", "Time horizon", "Context (archaeology)", "Computer science", "Operations research", "Route planning", "Routing (electronic design automation)", "Point (geometry)", "Set (abstract data type)", "Real-time computing", "Mathematical optimization", "Mathematics" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-40971-5_11" }, { "id": "https://openalex.org/W4400013179", "doi": "10.35534/dav.2024.10008", "title": "An Integer Programming Approach to Multi-Trip Routing of Delivery Drones at Load-Dependent Flight Speed", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Mao Nishira", "Hiroki Nishikawa", "Xiangbo Kong", "Hiroyuki Tomiyama" ], "first_author": "Mao Nishira", "venue": "Drones and autonomous vehicles/Drones and Autonomous Vehicles", "publisher": null, "abstract": "In recent years, there has been a growing interest in utilizing drones for parcel delivery among companies, aiming to address logistical challenges. However, effective optimization of delivery routes is essential. A theoretical framework termed the Flight Speed-aware Vehicle Routing Problem (FSVRP) has emerged to address the variability in drone flight speed based on payload weight. Several approximate methods have been proposed to solve the FSVRP. Our research endeavors to optimize parcel delivery efficiency and reduce delivery times by introducing a novel delivery problem. This problem accounts for multiple deliveries while considering the variability in flight speed due to diverse payloads. Through experimentation, we evaluate the efficacy of our proposed method compared to existing approaches. Specifically, we assess total flight distance and flight time. Our findings indicate that even in cases where the payload exceeds maximum capacity, all parcels can be delivered through multiple trips. Furthermore, employing a multi-trip FSVRP approach results in an average reduction of 10% in total flight time, even when payload capacities are not exceeded.", "keywords": [ "Drone", "Integer programming", "Routing (electronic design automation)", "Computer science", "Mathematical optimization", "Computer network", "Mathematics", "Algorithm", "Biology" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciepublish.com/index/article/view_full_text/id/220" }, { "id": "https://openalex.org/W4386554924", "doi": "10.48550/arxiv.2309.03397", "title": "Cooperative Multi-Agent Planning Framework for Fuel Constrained UAV-UGV Routing Problem", "year": 2023, "publication_date": "2023-09-06", "type": "preprint", "language": "en", "authors": [ "Md Safwan Mondal", "R. Subramanian", "James D. Humann", "Jean-Paul Reddinger", "James M. Dotterweich", "Marshal Childers", "Pranav A. Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned Aerial Vehicles (UAVs), although adept at aerial surveillance, are often constrained by limited battery capacity. By refueling on slow-moving Unmanned Ground Vehicles (UGVs), their operational endurance can be significantly enhanced. This paper explores the computationally complex problem of cooperative UAV-UGV routing for vast area surveillance within the speed and fuel constraints, presenting a sequential multi-agent planning framework for achieving feasible and optimally satisfactory solutions. By considering the UAV fuel limits and utilizing a minimum set cover algorithm, we determine UGV refueling stops, which in turn facilitate UGV route planning at the first step and through a task allocation technique and energy constrained vehicle routing problem modeling with time windows (E-VRPTW) we achieve the UAV route at the second step of the framework. The effectiveness of our multi-agent strategy is demonstrated through the implementation on 30 different task scenarios across 3 different scales. This work offers significant insight into the collaborative advantages of UAV-UGV systems and introduces heuristic approaches to bypass computational challenges and swiftly reach high-quality solutions.", "keywords": [ "Task (project management)", "Computer science", "Unmanned ground vehicle", "Heuristic", "Routing (electronic design automation)", "Vehicle routing problem", "Set (abstract data type)", "Distributed computing", "Real-time computing", "Artificial intelligence", "Engineering", "Systems engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2309.03397" }, { "id": "https://openalex.org/W1533073913", "doi": "10.1007/978-90-481-9707-1_79", "title": "UAV Logistic Support Definition", "year": 2014, "publication_date": "2014-08-09", "type": "book-chapter", "language": "en", "authors": [ "Sergio Chiesa", "Marco Fioriti" ], "first_author": "Sergio Chiesa", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Computer science" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-90-481-9707-1_79" }, { "id": "https://openalex.org/W4295356164", "doi": "10.48550/arxiv.2209.04057", "title": "Drone Deployment Optimization for Direct Delivery with Time Windows and Battery Replacements", "year": 2022, "publication_date": "2022-09-08", "type": "preprint", "language": "en", "authors": [ "Tanveer Hossain Bhuiyan", "Mohammad Roni", "Victor Walker" ], "first_author": "Tanveer Hossain Bhuiyan", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of goods to customers maintaining a specified time window. This paper presents a new mathematical optimization-based decision-making methodology to help business owners optimally route their drone fleet minimizing the total energy consumption, required fleet size, and the required number of additional batteries. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real drone flight tests and delivery data provide insights into the effect of different drone operating parameters on the energy consumption, required fleet size, and the required number of battery replacements. Results from a case study show that the total energy consumption, required fleet size, and the required number of battery replacements increase by 72.22%, 22.2%, and 200%, respectively, as the drones fly over the road networks compared to flying in a straight path. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.", "keywords": [ "Drone", "Quadcopter", "Battery (electricity)", "Software deployment", "Energy consumption", "Computer science", "Automotive engineering", "Simulation", "Real-time computing", "Engineering", "Electrical engineering", "Aerospace engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2209.04057" }, { "id": "https://openalex.org/W4411628128", "doi": "10.1016/j.trc.2025.105189", "title": "Collaborative vessel–unmanned aerial vehicle routing for time-window-constrained offshore parcel delivery", "year": 2025, "publication_date": "2025-06-25", "type": "article", "language": "en", "authors": [ "Yantong Li", "Shengjie Wang", "Hairui Sun", "Shanshan Zhou" ], "first_author": "Yantong Li", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Submarine pipeline", "Window (computing)", "Marine engineering", "Vehicle routing problem", "Routing (electronic design automation)", "Transport engineering", "Computer science", "Aeronautics", "Environmental science", "Engineering", "Real-time computing", "Computer network" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2025.105189" }, { "id": "https://openalex.org/W2900395175", "doi": "10.1007/978-3-030-01057-7_85", "title": "Application of Deep Reinforcement Learning to UAV Fleet Control", "year": 2018, "publication_date": "2018-11-07", "type": "conference-paper", "language": "en", "authors": [ "Jan Tožička", "Benedek Szulyovszky", "Guillaume de Chambrier", "Varun Sarwal", "Umar Wani", "Mantas Gribulis" ], "first_author": "Jan Tožička", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Reinforcement learning", "Computer science", "Artificial intelligence", "Control (management)" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-01057-7_85" }, { "id": "https://openalex.org/W4415452541", "doi": "10.1016/j.trc.2025.105389", "title": "Multi-trip UAV-UGV delivery network design with release times", "year": 2025, "publication_date": "2025-10-23", "type": "article", "language": "en", "authors": [ "Bolong Zhou", "Wenjia Zeng", "Hai Long Yang" ], "first_author": "Bolong Zhou", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Linear programming", "Swift", "Vehicle routing problem", "Heuristic", "Flow network", "Routing (electronic design automation)", "Network planning and design" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2025.105389" }, { "id": "https://openalex.org/W7169789253", "doi": "10.1016/j.eswa.2026.133716", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating multi-factor energy-consumption inputs and a partial recharging strategy", "year": 2026, "publication_date": "2026-07-20", "type": "article", "language": "en", "authors": [ "Shuai Zhang", "Yue Wu", "Wanting Chen", "Yong Chen" ], "first_author": "Shuai Zhang", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Key (lock)", "Electric vehicle", "Multipath routing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2026.133716" }, { "id": "https://openalex.org/W4407638139", "doi": "10.1109/access.2025.3543007", "title": "Employing Hybrid Pointer Networks With Deep Reinforcement Learning for Drone Routing in Delivery Using Public Transportation as Carriers", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Mahmoud Masoud", "Omar Ashraf Ibrahim", "Mohammed Mamdouh Zakaria Elhenawy" ], "first_author": "Mahmoud Masoud", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Since the inception of Autonomous Aerial Vehicles (AAVs), also known as drones, and the rapid growth of the AAV industry, significant steps have been taken to design drones for diverse applications. Particularly in the last decade, the industry’s progress has been so swift that novel uses for drones continue to emerge regularly. One field that has demonstrated promising potential for future applications is drone-based delivery services. However, a notable challenge in utilizing drones for deliveries is their limited operational range. To address this challenge, we propose a machine learning model that leverages public transportation vehicles as carriers to extend the range of drones, integrating Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL) to solve the complex routing problem. The primary objective is to minimize the total flying distance covered by drones while adhering to the capacity limitations and scheduled routes of public transportation buses. Notably, our model ensures that no drone is serviced if the bus roof capacity is fully utilized. By employing HPNs, we effectively manage complicated interactions between drones, buses, and delivery points, simplifying the optimization process to achieve cutting-edge results. The model was carefully optimized and fine-tuned after being trained in a simulated environment to maximize its performance. Results show that combining drone delivery services with bus service significantly lowers energy usage and improves the system’s capacity to fulfill delivery obligations.", "keywords": [ "Reinforcement learning", "Drone", "Computer science", "Pointer (user interface)", "Routing (electronic design automation)", "Computer network", "Artificial intelligence", "Human–computer interaction" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2025.3543007" }, { "id": "https://openalex.org/W3198901053", "doi": "10.1007/978-3-030-75067-1_19", "title": "Intelligent and Fuzzy UAV Transportation Applications in Aviation 4.0", "year": 2021, "publication_date": "2021-08-26", "type": "book-chapter", "language": "en", "authors": [ "Mahmoud Golabi", "Mazyar Ghadiri Nejad" ], "first_author": "Mahmoud Golabi", "venue": "Studies in systems, decision and control", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Aviation", "Fuzzy logic", "Computer science", "Operations research", "Path (computing)", "Genetic algorithm", "Systems engineering", "Engineering", "Artificial intelligence", "Machine learning", "Aerospace engineering" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-75067-1_19" }, { "id": "https://openalex.org/W3038608082", "doi": "10.1109/vtc2020-spring48590.2020.9128632", "title": "Construction Of A Temporary Message Collection System Using A Drone For Refugees In A Large-Scale Disaster", "year": 2020, "publication_date": "2020-05-01", "type": "conference-paper", "language": "en", "authors": [ "Azusa Danjo", "Atsuki Murata", "Shinsuke Hara", "Takahiro Matsuda", "Fumie Ono" ], "first_author": "Azusa Danjo", "venue": null, "publisher": null, "abstract": "When a large-scale disaster happens, which causes catastrophic damage to terrestrial information/road traffic networks and forces a huge number of people to live as refugees in shelters, it is necessary to quickly check the safety of victims and damage state in the disaster-hit area. Unmanned Aerial Vehicles (UAVs) including drones are promised to play an essential role in such a disaster recovery process because of its infrastructureless workability.In this paper, we propose a message collection system using a drone for refugees in a disaster-hit area. In order to quickly and efficiently collect messages for all refugees dispersed in shelters, we try to solve a problem to create a flight plan in the form of the Vehicle Routing Problem (VRP), taking into consideration two major drawbacks of drone such as short time to fly and vulnerability to wind. Our numerical results for an area of Osaka, Japan, show that off-line pre-creation of drone routes assuming no wind is risky, because the drone cannot safely return to a depot, if it is used under some windy conditions.", "keywords": [ "Drone", "Refugee", "Computer science", "Vulnerability (computing)", "Computer security", "Scale (ratio)", "Aeronautics", "Engineering", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/vtc2020-spring48590.2020.9128632" }, { "id": "https://openalex.org/W4213299032", "doi": "10.3390/drones6020051", "title": "Homogeneous Agent Behaviours for the Multi-Agent Simultaneous Searching and Routing Problem", "year": 2022, "publication_date": "2022-02-17", "type": "article", "language": "en", "authors": [ "Thomas Kent", "Arthur Richards", "Angus Johnson" ], "first_author": "Thomas Kent", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Through the use of autonomy Unmanned Aerial Vehicles (UAVs) can be used to solve a range of of multi-agent problems that exist in the real world, for example search and rescue or surveillance. Within these scenarios the global objective might often be better achieved if aspects of the problem can be optimally shared amongst its agents. However, in uncertain, dynamic and often partially observable environments centralised global-optimisation techniques are not achievable. Instead, agents may have to act on their own belief of the world, making the best decisions independently and potentially myopically. With multiple agents acting in a decentralised manner how can we discourage competitive behaviour and instead facilitate cooperation. This paper focuses on the specific problem of multiple UAVs simultaneously searching for tasks in an environment whilst efficiently routing between them and ultimately visiting them. This paper is motivated by this idea that collaboration can be simple and achieved without the need for a dialogue but instead through the design of the individual agent’s behaviour. By focusing on what is communicated we expand the use of a single agent behaviour. Which through minor modifications can produce distinct agents demonstrating independent, collaborative and competitive behaviour. In particular by investigating the role of sensor and communication ranges this paper will show that increased sensor ranges can be detrimental to system performance, and instead the simple modelling of nearby agents’ intent is a far better approach.", "keywords": [ "Computer science", "Autonomous agent", "Simple (philosophy)", "Multi-agent system", "Variety (cybernetics)", "Homogeneous", "Routing (electronic design automation)", "Distributed computing", "Autonomy", "Range (aeronautics)", "Artificial intelligence", "Engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/6/2/51/pdf?version=1645282074" }, { "id": "https://openalex.org/W4411233509", "doi": "10.1109/sustech63138.2025.11025538", "title": "Monitoring agricultural fields through collaboration between autonomous robots and drones", "year": 2025, "publication_date": "2025-04-20", "type": "conference-paper", "language": "en", "authors": [ "Alessia Ciacco", "Giovanni Giallombardo", "Francesca Guerriero", "Francesco Paolo Saccomanno" ], "first_author": "Alessia Ciacco", "venue": null, "publisher": null, "abstract": "In recent years, precision agriculture has emerged as one of the most promising answers to global challenges related to food security, production efficiency and environmental sustainability. This innovative approach uses advanced technologies to optimize the use of agricultural resources, reduce waste and improve crop yields. Among these technologies, autonomous vehicles, both robots and drones, play a key role in revolutionizing basic operations such as monitoring, data collection, irrigation and fertilizer application. In particular, collaboration between robots and drones for monitoring agricultural fields is a promising solution. This paper proposes a Mixed Integer Linear Programming model to address three key issues in agriculture: soil monitoring, fertilization and plant irrigation. Monitoring of agricultural fields is conducted through a network of distributed sensors, with data collected by autonomous robot and drone. Furthermore, the robot is responsible for fertilization activities, while the drone performs irrigation. The objective of the model is to minimize the total cost, which includes the cost associated with vehicle routing and the cost of sensor activation, considering limitations related to battery life and sensor override management.", "keywords": [ "Drone", "Robot", "Agriculture", "Computer science", "Mobile robot", "Human–computer interaction", "Engineering", "Aeronautics", "Artificial intelligence", "Ecology", "Biology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/sustech63138.2025.11025538" }, { "id": "https://openalex.org/W4320802476", "doi": "10.1109/ccdc55256.2022.10034189", "title": "Multi-objective traveling salesman problem with drone: imperialist competitive algorithm", "year": 2022, "publication_date": "2022-08-15", "type": "conference-paper", "language": "en", "authors": [ "Huan Xiong", "Deming Lei", "Ming Li" ], "first_author": "Huan Xiong", "venue": "2022 34th Chinese Control and Decision Conference (CCDC)", "publisher": null, "abstract": "Truck-drone delivery is a hybrid one combining the advantages of truck including large capacity and long travel distance and those of drone like high speed together and attracting much attention in the past several years. The usage of this delivery leads to some generalized routing problems such as traveling salesman problem with drone (TSP-D). TSP-D has been considered fully since the pioneering work of Murray and Chu (2015); however, multi-objective TSP-D is seldom investigated and the applications of meta-heuristics are limited. In this paper, multi-objective TSP-D is addressed and an imperialist competitive algorithm with empire cooperation (ECICA) is developed to minimize operational cost and completion time. In ECICA, a coding method with a truck’s path and a set of drone nodes and an effective decoding are presented, and an adaptive empire cooperation between the strongest empire and the weakest one is implemented by exchanging computing resources and search ability. Historical evolution data are used and a new imperialist competition is adopted. Numerical experiments are conducted on various instances. ECICA is compared with comparative algorithms from the literature. The computational results reveal that ECICA has promising advantages on solving multi-objective TSP-D.", "keywords": [ "Travelling salesman problem", "Drone", "Heuristics", "Computer science", "Truck", "2-opt", "Mathematical optimization", "Algorithm", "Mathematics", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccdc55256.2022.10034189" }, { "id": "https://openalex.org/W3143038773", "doi": "10.36001/phmconf.2013.v5i1.2294", "title": "A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning", "year": 2013, "publication_date": "2013-10-14", "type": "conference-paper", "language": "en", "authors": [ "Edward Balaban", "Juan Jose Alonso" ], "first_author": "Edward Balaban", "venue": "Annual Conference of the PHM Society", "publisher": null, "abstract": "The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in PDM is find- ing a sufficiently expressive yet compact mathematical representation of the system for use with decision optimization algorithms. In this paper we describe a general modeling approach for a class of PDM problems with non-linear system degradation processes and uncertainties in state estimation, action effects, and future operating conditions. The approach is based on continuous Partially Observable Markov Decision Processes (POMDPs) used in conjunction with ’black box’ system simulations. The proposed modeling framework can be cast into simpler representations, depending on which sources of uncertainty are being included. The approach is illustrated with a mission planning case study for an unmanned aerial vehicle (UAV). In the case study a PDM system is tasked with optimizing the vehicle route after an in-flight component fault is detected. A stochastic algorithm (based on particle filtering) is used for decision optimization, with a second, deterministic algorithm providing a performance evaluation baseline. Both algorithms utilize a UAV physics simulator for generating predictions of future vehicle states. Performance benchmarking is done on a set of mission scenarios of increasing complexity.", "keywords": [ "Benchmarking", "Computer science", "Baseline (sea)", "Markov decision process", "Set (abstract data type)", "Representation (politics)", "Key (lock)", "System dynamics", "Black box", "Component (thermodynamics)", "Operations research", "Mathematical optimization" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://papers.phmsociety.org/index.php/phmconf/article/download/2294/1276" }, { "id": "https://openalex.org/W1963558436", "doi": "10.2514/6.2006-6106", "title": "Formulation and Solution of the UAV Paparazzi Problem", "year": 2006, "publication_date": "2006-06-15", "type": "conference-paper", "language": "en", "authors": [ "Petter Ögren", "S.-L. Wirkander", "Anna Stefansson", "Johan Pelo" ], "first_author": "Petter Ögren", "venue": "AIAA Guidance, Navigation, and Control Conference and Exhibit", "publisher": null, "abstract": "Many UAV path planning problems dealing with multiple targets are formulated as instances of classical optimization problems such as the Traveling Salesperson Problem (TSP), Vehicle Routing Problem (VRP), and the Assignment Problem (AP). In their basic forms, all these problems minimize the length of the flight path to be flown by the UAVs. Even though this is often a reasonable choice, we argue that in instances when the targets are able to hide, or move, an objective function accounting for the target value, as well as the chance of actually finding the target is more natural. In some sense, the problem is similar to the one faced by a Paparazzi photographer, deciding where to go to get snapshots of non cooperative celebrities. Hence we formulate the so-called Paparazzi utility function, and show how it can be applied to UAV path planning problems similar to the TSP, VRP and AP.", "keywords": [ "Computer science", "Aeronautics", "Engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2006-6106" }, { "id": "https://openalex.org/W3156120014", "doi": "10.11121/ijocta.01.2021.001023", "title": "UAV routing with genetic algorithm based matheuristic for border security missions", "year": 2021, "publication_date": "2021-04-19", "type": "article", "language": "en", "authors": [ "Ömer Özkan", "Muhammed Kaya" ], "first_author": "Ömer Özkan", "venue": "An International Journal of Optimization and Control Theories & Applications (IJOCTA)", "publisher": "Balıkesir University", "abstract": "In recent years, Unmanned Aerial Vehicles (UAVs) are a good alternative for the problem of ensuring the security of the borders of the countries. UAVs are preferred because of their speed, ease of use, being able to observe many points at the same time, and being more cost-effective in total compared to other security tools. This study is dealt with the problem of the use of UAVs for the security of the Turkey-Syria borderline which becomes sensitive in recent years and the problem is modeled as a UAV routing problem. To solve the problem, a Genetic Algorithm Based Matheuristic (GABM) approach has been developed and 12 scenarios have been created covering the departure bases, daily patrol numbers, and ranges of UAVs. GABM finds the minimum number of UAVs to use in scenarios with the help of a GA run first and tries to find the optimal routes for these UAVs. If GABM can find an optimal route for the determined UAV number, it decreases the UAV number and tries to solve the problem again. GABM proposes a hybrid approach in which a metaheuristic with a mathematical model works together and the metaheuristic sets an upper limit for the number of UAVs in the model. In computational studies, when compared GA with GABM it is seen that GABM has obtained good results and decreased the utilized number of UAVs (up to 400%) and their flight distances (up to 85.99%) for the problem in very short CPU times (max. 122.17 s. for GA and max. 46.39 s. for GABM in addition to GA).", "keywords": [ "Computer science", "Genetic algorithm", "Routing (electronic design automation)", "Metaheuristic", "Mathematical optimization", "Limit (mathematics)", "Vehicle routing problem", "Algorithm", "Mathematics", "Computer network", "Machine learning" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "http://ijocta.org/index.php/files/article/download/1023/352" }, { "id": "https://openalex.org/W4404800849", "doi": "10.1016/j.sca.2024.100094", "title": "A novel sequential block path planning method for 3D unmanned aerial vehicle routing in sustainable supply chains", "year": 2024, "publication_date": "2024-11-29", "type": "article", "language": "en", "authors": [ "Muhammad Nabeel Ikram", "Robert P. Sroufe" ], "first_author": "Muhammad Nabeel Ikram", "venue": "Supply Chain Analytics", "publisher": "Elsevier BV", "abstract": "Managing sustainable supply chain operations in dynamic three-dimensional (3D) environments is a significant challenge. Unmanned Aerial Vehicles (UAVs) offer transformative solutions to supply chains. This study aims to enhance sustainable supply chain management by considering new opportunities for optimizing UAV networks. The primary objective is to develop advanced path planning and routing algorithms that improve the quality of service in a supply chain. We present a novel Sequential Block Path Planning (SBPP) method, a modified version of the heuristic D* Lite algorithm, to achieve the shortest logistics path with reduced computation time. We utilize queuing theory for task scheduling and UAV assignments within a supply chain network while ensuring efficient and effective task distribution. The results demonstrate that the proposed combination of routing and path planning algorithms significantly improves performance in 3D environments, resulting in shorter logistics paths, enhanced quality of service, and reduced computation time. The outcomes of this study represent a substantial contribution to UAV network management, particularly in terms of efficiency and operational effectiveness. The novel approach utilized in this study contributes to the emerging UAV field in supply chains and enhances sustainability and operational efficiency in logistics networks.", "keywords": [ "Block (permutation group theory)", "Path (computing)", "Vehicle routing problem", "Supply chain", "Motion planning", "Routing (electronic design automation)", "Computer science", "Environmental science", "Business", "Artificial intelligence", "Robot", "Computer network" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.sca.2024.100094" }, { "id": "https://openalex.org/W4410909146", "doi": "10.1007/s10846-025-02277-6", "title": "A Multi-UAV Approach for Fast Inspection of Overhead Power Lines: From Route Planning to Field Operation", "year": 2025, "publication_date": "2025-05-31", "type": "article", "language": "en", "authors": [ "Alvaro Caballero", "Francisco Javier Roman-Escorza", "Iván Maza", "Anı́bal Ollero" ], "first_author": "Alvaro Caballero", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": "Abstract Overhead power lines are critical infrastructures to ensure a reliable energy supply, and failures in the grid can lead to significant service disruptions. Locating these faults quickly is crucial but often challenging, especially in hard-to-reach areas such as mountainous regions. This paper presents an integrated solution for the long-range visual inspection of overhead power lines in minimum time using teams of Unmanned Aerial Vehicles (UAVs). The solution, designed for effective field operation while meeting end-user requirements, comprises route planning, autonomous execution, and monitoring of the inspection mission. Concerning route planning, a capacitated min-max multi-depot vehicle routing problem has been formulated to compute feasible routes that cover the entire grid in minimum mission time. The method can be applied to heterogeneous multi-UAV teams in terms of inspection speed and battery consumption, which helps maximise the utilisation of available robots. Moreover, the planning method is complemented by an accurate battery-consumption model based on energy principles that captures the effect of parameters often overlooked such as UAV mass, inspection speed, and weather conditions. The model has shown estimates with relative errors not exceeding 1.34% compared to real measurements. The proposed solution has been experimentally validated under real-world conditions, enabling the autonomous multi-UAV inspection of more than 10 kilometres of real power lines in 13 minutes, which represents a time reduction of up to 67.21% compared to the state of the art. The resulting videos enabled the identification of a simulated power outage and its exact location.", "keywords": [ "Overhead (engineering)", "Field (mathematics)", "Computer science", "Power (physics)", "Real-time computing", "Embedded system", "Operating system", "Mathematics" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10846-025-02277-6.pdf" }, { "id": "https://openalex.org/W7153728707", "doi": "10.1016/j.eswa.2026.132407", "title": "Multi-objective optimization of drone location-routing problem for photovoltaic power plant inspection: a MOEA/D-based approach with altitude coordination", "year": 2026, "publication_date": "2026-04-12", "type": "article", "language": "en", "authors": [ "Lian Bai", "Yaoyun Zhang", "Shuhui Yang", "Zhao Zhang", "Yong Liu" ], "first_author": "Lian Bai", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Photovoltaic system", "Power (physics)", "Altitude (triangle)", "Effects of high altitude on humans" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2026.132407" }, { "id": "https://openalex.org/W4319778697", "doi": "10.1109/dsaa54385.2022.10032441", "title": "The min-timespan parallel technician-and-drone scheduling in door-to-door sampling service system", "year": 2022, "publication_date": "2022-10-13", "type": "conference-paper", "language": "en", "authors": [ "Pham Phu Manh", "Tran Thi Hue", "Huỳnh Thị Thanh Bình", "Nguyen Khanh Phuong" ], "first_author": "Pham Phu Manh", "venue": "2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)", "publisher": null, "abstract": "This paper considers a variant of the Vehicle Routing Problem with Drones applied in the door-to-door sampling service system. Given a set of technicians and a fleet of drones departing from a medical center and working independently, the objective consists in designing feasible trips for technicians and drones such that the time from collection to arrival at the medical center of each customer’s test kits is not exceeded a limited duration and minimal makespan is achieved. A mixed-integer linear program (MILP) model is presented and solved to optimality with small instances due to the computational complexity of this problem. Thus, a tabu search approach is proposed. Experiments are then carried out to demonstrate the performance of the proposed algorithm and the advantage of integrating drones into the sampling service system.", "keywords": [ "Drone", "Computer science", "Job shop scheduling", "Scheduling (production processes)", "Integer programming", "Tabu search", "Sampling (signal processing)", "Service (business)", "Linear programming", "Technician", "Mathematical optimization", "Real-time computing" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/dsaa54385.2022.10032441" }, { "id": "https://openalex.org/W2101726199", "doi": "10.1093/logcom/exr028", "title": "Towards a model of UAVs navigation in urban canyon through defeasible logic", "year": 2012, "publication_date": "2012-03-01", "type": "article", "language": "en", "authors": [ "Ho-Pun Lam", "Guido Governatori" ], "first_author": "Ho-Pun Lam", "venue": "Journal of Logic and Computation", "publisher": "Oxford University Press", "abstract": "This article shows how a non-monotonic rule based system (defeasible logic) can be integrated with numerical computation engines, and how this can be applied to solve the Vehicle Routing Problem. To this end, we have simulated a physical system from which we can obtain numerical information. The physical system perceives information from its environment and generates predicates that can be reasoned by a defeasible logic engine. The conclusions/decisions derived will then be realized by the physical system as it takes actions based on the conclusion derived. Here we consider a scenario where a ‘flock’ of Unmanned Autonomous Vehicles (UAVs) have to navigate within an urban canyon environment. The UAVs are self-autonomous without centralized control. The goal of the UAVs is to navigate to their desired destinations without colliding with each other. In case of possible collision, the UAVs concerned will communicate with each other and use their background knowledge or travel guidelines to resolve the conflicts.", "keywords": [ "Defeasible estate", "Computer science", "Canyon", "Routing (electronic design automation)", "Inference engine", "Monotonic function", "Artificial intelligence", "Inference", "Computer network", "Geography", "Mathematics" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1093/logcom/exr028" }, { "id": "https://openalex.org/W2214970019", "doi": "10.5505/pajes.2013.25733", "title": "Literature Review of Dynamic Unmanned Aerial System Routing Problems and Proposals For Future Studies of UASs", "year": 2013, "publication_date": "2013-01-01", "type": "article", "language": "en", "authors": [ "Cihan Ercan", "Cevriye Gencer" ], "first_author": "Cihan Ercan", "venue": "Pamukkale University Journal of Engineering Sciences", "publisher": "Pamukkale University", "abstract": "İnsansız Hava Sistemleri (İHS) ile sağlanan teknolojik olanaklar kadar önemli olan bir husus da, kullanıcının bu olanakları etkin şekilde planlayabilmesi, ihtiyacı olan anlık değişikliğe çabuk ayak uydurabilmesi, karar vericiler için önemli olabilecek bilgiyi doğru ve zamanında paylaşabilmesi, kısaca ağ üzerinde verimli çalışabilmesidir.Muhabere Elektronik ve Bilgi Sistemleri (MEBS)'ndeki gelişmeler, Araç Rotalama Probleminin (ARP) dinamik durumlarda da çözümünü mümkün kılmıştır.Bu bağlamda İHS'lerinin, keşif faaliyetleri rota planlanması kapsamındaki ARP uygulamaları her geçen gün gelişerek artmaktadır.Bu makalenin amacı, İHS'lerinin \"dinamik rota planlaması\" kapsamında yapılan literatür çalışmalarının incelenmesi, kapsamlarının tespiti, bu alandaki gelişmeler ile çalışılmayan alanların belirlenmesi ve böylelikle ileride araştırma yapılabilecek alanlara yön verilmesidir.Bu sayede, İHS'lerin havada kalış süresinin artırılmasından ziyade, havada kaldığı müddetçe yol için ayrılan \"niteliksiz\" zamanın azaltılarak, hedefler üzerindeki görevini icra ettiği \"nitelikli\" zamanın artırılması hedeflenmektedir.Outwith the technological developments made with Unmanned Aerial Vehicles (UAV); other important issues for the users like effective planning and re-planning; providing the clear, concise and timely information to the decision makers is part of the Network Enabled Capability.Significant improvements to the Communication and Information systems have made it possible to find dynamic solutions for Vehicle Routing Problems.In this context, \"Vehicle Routing\" applications for UAVs in reconnaissance missions are increasing exponentially.This study investigates the literature in \"dynamic route planning\", defining the scope and identifying shortcomings for future studies in Unmanned Aerial Systems.Using this approach not only reduces stagnant travel time to target time but increases the usable times spent on targets.", "keywords": [ "Humanities", "Computer science", "Art", "Political science" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://jag.journalagent.com/z4/download_fulltext.asp?pdir=pajes&plng=eng&un=PAJES-25733" }, { "id": "https://openalex.org/W2811501059", "doi": "10.1145/3219788.3219802", "title": "Express UAV Swarm Path Planning with VND Enhanced Memetic Algorithm", "year": 2018, "publication_date": "2018-05-04", "type": "conference-paper", "language": "en", "authors": [ "Fanli Duan", "Xiaohui Li", "Yi Zhao" ], "first_author": "Fanli Duan", "venue": null, "publisher": null, "abstract": "Current goods delivery are mostly based on human couriers. It is time consuming, labor intensive and low efficient. Therefore, lot of Logistics Companies are making efforts in studying the possibility of employing express UAV in good delivery. One key technique issue of the UAV delivery system is its capacity limitation. It is known as the Capacity vehicle routing problem (CVRP), which is indeed a NP-hard problem. In this work, a novel hybrid metaheuristic approach is proposed aiming at this problem. It combines both MA and VND (variable neighborhood descend) algorithms. The simulation results proved the effectiveness of our proposed algorithm.", "keywords": [ "Vehicle routing problem", "Computer science", "Memetic algorithm", "Key (lock)", "Metaheuristic", "Swarm behaviour", "Variable (mathematics)", "Mathematical optimization", "Path (computing)", "Routing (electronic design automation)", "Algorithm", "Work (physics)" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3219788.3219802" }, { "id": "https://openalex.org/W4417224413", "doi": "10.21203/rs.3.rs-8221874/v1", "title": "Research on Location-Routing Problem for Logistics UAVs Based on Bi-layer Programming", "year": 2025, "publication_date": "2025-12-11", "type": "preprint", "language": null, "authors": [ "Jiangao Zhang", "Suyin Cui", "Pei Zhu", "Liya Wang", "Zhi Sun", "Quan Shao" ], "first_author": "Jiangao Zhang", "venue": "Research Square", "publisher": null, "abstract": null, "keywords": [ "Adaptability", "Robustness (evolution)", "Scalability", "Benchmark (surveying)", "Drone", "Key (lock)", "Path (computing)", "Flexibility (engineering)", "Range (aeronautics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-8221874/latest.pdf" }, { "id": "https://openalex.org/W7128441117", "doi": "10.1016/j.swevo.2026.102309", "title": "A robust bi-objective optimization model and heuristic solution for truck-drone collaboration in humanitarian logistics under truck travel time uncertainty", "year": 2026, "publication_date": "2026-02-01", "type": "article", "language": "en", "authors": [ "Huizhen Zhang", "Chaoyue Pan", "Mitsuo Gen" ], "first_author": "Huizhen Zhang", "venue": "Swarm and Evolutionary Computation", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Drone", "Vehicle routing problem", "Simulated annealing", "Metaheuristic", "Robust optimization", "Heuristic", "Routing (electronic design automation)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.swevo.2026.102309" }, { "id": "https://openalex.org/W2087794076", "doi": "10.1117/12.718888", "title": "A UAV routing and sensor control optimization algorithm for target search", "year": 2007, "publication_date": "2007-04-27", "type": "conference-paper", "language": "en", "authors": [ "Gaemus E. Collins", "James R. Riehl", "Philip S. Vegdahl" ], "first_author": "Gaemus E. Collins", "venue": "Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE", "publisher": "SPIE", "abstract": "An important problem in unmanned air vehicle (UAV) and UAV-mounted sensor control is the target search problem: locating target(s) in minimum time. Current methods solve the optimization of UAV routing control and sensor management independently. While this decoupled approach makes the target search problem computationally tractable, it is suboptimal. In this paper, we explore the target search and classification problems by formulating and solving a joint UAV routing and sensor control optimization problem. The routing problem is solved on a graph using receding horizon optimal control. The graph is dynamically adjusted based on the target probability distribution function (PDF). The objective function for the routing optimization is the solution of a sensor control optimization problem. An optimal sensor schedule (in the sense of maximizing the viewed target probability mass) is constructed for each candidate flight path in the routing control problem. The PDF of the target state is represented with a particle filter and an \"occupancy map\" for any undiscovered targets. The tradeoff between searching for undiscovered targets and locating tracks is handled automatically and dynamically by the use of an appropriate objective function. In particular, the objective function is based on the expected amount of target probability mass to be viewed.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Optimization problem", "Mathematical optimization", "Schedule", "Wireless sensor network", "Algorithm", "Mathematics" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.718888" }, { "id": "https://openalex.org/W4282959099", "doi": "10.1177/1748006x221105395", "title": "A two-stage UAV routing problem with time window considering rescheduling with random delivery reliability", "year": 2022, "publication_date": "2022-06-14", "type": "article", "language": "en", "authors": [ "Rui Qi Yan", "Haotong Tian", "Kaiye Gao", "Rui Peng", "Bin Liu" ], "first_author": "Rui Qi Yan", "venue": "Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability", "publisher": "SAGE Publishing", "abstract": "Reliability is an important metric for unmanned aerial vehicles (UAVs) to perform important, complex, and dangerous tasks. In addition, reliability influences the operational cost in UAVs routing. In order to reduce the operational cost of UAVs tasks, a method of routing strategy optimization is proposed from a view of mission reliability in this paper to address the limitations in routing scheduling. Different from previous studies on the UAVs routing optimization problem, this paper proposes a method that can reduce the operational costs of UAVs tasks based on the mission reliability. This method includes two stages: the pre-optimization stage and the rescheduling stage. In the pre-optimization stage, an optimal UAVs route solution is obtained for all the targets, while in the rescheduling stage, new UAVs are dispatched to the unvisited targets in the pre-optimization stage based on the new optimal UAVs route.", "keywords": [ "Reliability (semiconductor)", "Computer science", "Routing (electronic design automation)", "Scheduling (production processes)", "Metric (unit)", "Reliability engineering", "Real-time computing", "Engineering", "Computer network", "Operations management" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/1748006x221105395" }, { "id": "https://openalex.org/W3089027861", "doi": "10.1007/978-981-15-6619-6_34", "title": "A Route Planning Strategy for Commercial Deliveries Using Drones", "year": 2020, "publication_date": "2020-09-26", "type": "book-chapter", "language": "en", "authors": [ "Soumen Manna", "Anand M. Narasimhamurthy" ], "first_author": "Soumen Manna", "venue": "Lecture notes in mechanical engineering", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Heuristics", "Scalability", "Vehicle routing problem", "Computer science", "Scheduling (production processes)", "Route planning", "Computation", "Overhead (engineering)", "Truck", "Greedy algorithm", "Routing (electronic design automation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-15-6619-6_34" }, { "id": "https://openalex.org/W4413468664", "doi": "10.2139/ssrn.5393526", "title": "Dynamic Order Fulfillment in Last-Mile Drone Delivery Under Demand Uncertainty", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Linxuan Shi", "Zhengtian Xu", "Miguel A. Lejeune", "Qi Luo" ], "first_author": "Linxuan Shi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Order (exchange)", "Computer science", "Aeronautics", "Operations research", "Business", "Engineering", "Geography", "Finance" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5393526" }, { "id": "https://openalex.org/W7155551997", "doi": "10.1016/j.cor.2026.107504", "title": "An attention-enhanced meta-heuristic algorithm for multi-drone assisted humanitarian delivery problem", "year": 2026, "publication_date": "2026-04-24", "type": "article", "language": "en", "authors": [ "Shuang Liang", "Y L Chen", "Zongcheng Zhang", "M.I.M. Wahab", "Qinxin Xiao" ], "first_author": "Shuang Liang", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Key (lock)", "Truck", "Vehicle routing problem", "Routing (electronic design automation)", "Linear programming" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2026.107504" }, { "id": "https://openalex.org/W2540003298", "doi": "10.1109/cgncc.2014.7007214", "title": "Autonomous route planning for UAV when threats are uncertain", "year": 2014, "publication_date": "2014-08-01", "type": "conference-paper", "language": "en", "authors": [ "Jian Wu", "Donghao Zhang", "Denghong Pei" ], "first_author": "Jian Wu", "venue": null, "publisher": null, "abstract": "Route planning in 3-D uncertainty environment, especially when the threats are uncertain, is the trend of the UAV (unmanned aerial vehicle) route planning. Against the opacity of enemy threat information, the threat probability model is adopted to determine the location of the threat, and D-S evidence synthesis algorithm is used to determine the threat strength, then to calls D* route planning algorithm getting UAV flight route and forming the \"flying, judging and planning\" autonomous planning pattern. Paper also analyzes various threats and then establishes 3-D models for these threats. The method turns out to be feasible by simulation.", "keywords": [ "Adversary", "Route planning", "Computer science", "Motion planning", "Operations research", "Computer security", "Artificial intelligence", "Engineering", "Transport engineering", "Robot" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cgncc.2014.7007214" }, { "id": "https://openalex.org/W4382519406", "doi": "10.1007/978-3-031-36808-0_10", "title": "Investigating Alternative Clustering Algorithms for a Cluster-First, Route-Second Truck and Drone Scheduling Algorithm", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Sarah Dillon", "Rudolf Ernst", "Jacomine Grobler" ], "first_author": "Sarah Dillon", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Cluster analysis", "Computer science", "Drone", "Vehicle routing problem", "Travelling salesman problem", "Algorithm", "Truck", "Data mining", "Mathematical optimization", "Routing (electronic design automation)", "Artificial intelligence", "Mathematics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-36808-0_10" }, { "id": "https://openalex.org/W262832597", "doi": null, "title": "A Java Universal Vehicle Router in Support of Routing Unmanned Aerial Vehicles", "year": 2012, "publication_date": "2012-10-02", "type": "article", "language": "en", "authors": [ "Robert W. Harder" ], "first_author": "Robert W. Harder", "venue": "Defense Technical Information Center (DTIC)", "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs) help the military gather information in times of peace and war. During a mission, typically 100 sites or more, a UAV will frequently be re-tasked to visit a pop-up threat, leaving the operator to determine the best way to finish the day's list of sites after the re-tasking. I develop a prototype application to serve the needs of a specific customer, the 11th Reconnaissance Squadron, by helping them preplan missions and dynamically re-task UAVs. This prototype application is built on a reusable airframe router called the core AFIT Router, which can later be added to more sophisticated mapping and planning software for other customers. The core AFIT Router is built on a new architecture, defined and implemented in this research, which calls for tools that solve entire classes of problems. To support the UAV routing problem, I develop such an architecture for Vehicle Routing Problems (VRPs) and Traveling Salesman Problems (TSPs) and call it the Universal Vehicle Router (UVR). The UVR allows for many solving techniques to be plugged in, and two sample solvers are included, one a tour-building heuristic by Gary Kinney and the other an adaptive tabu search developed in this research.", "keywords": [ "Router", "Computer science", "Routing (electronic design automation)", "Architecture", "Vehicle routing problem", "Heuristic", "Travelling salesman problem", "Embedded system", "Computer network", "Artificial intelligence", "Geography" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA378105" }, { "id": "https://openalex.org/W2944427273", "doi": "10.1007/s10846-019-01027-9", "title": "Multi-UAV Trajectory Optimization Utilizing a NURBS-Based Terrain Model for an Aerial Imaging Mission", "year": 2019, "publication_date": "2019-05-07", "type": "article", "language": "en", "authors": [ "Youngjun Choi", "Youngjun Choi", "Mengzhen Chen", "Younghoon Choi", "Younghoon Choi", "Simon I. Briceño", "Dimitri N. Mavris" ], "first_author": "Youngjun Choi", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Terrain", "Trajectory", "Trajectory optimization", "Point cloud", "Computer science", "Lidar", "Computer vision", "Real-time computing", "Artificial intelligence", "Remote sensing", "Geography" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10846-019-01027-9" }, { "id": "https://openalex.org/W4313269855", "doi": "10.1109/icus55513.2022.9987158", "title": "Cooperative Route Planning for Fuel-constrained UGV-UAV Exploration", "year": 2022, "publication_date": "2022-10-28", "type": "conference-paper", "language": "en", "authors": [ "Mingjia Zhang", "Huawei Liang", "Pengfei Zhou" ], "first_author": "Mingjia Zhang", "venue": "2022 IEEE International Conference on Unmanned Systems (ICUS)", "publisher": null, "abstract": "Heterogeneous UGV-UAV system can efficiently complete large-scale tasks such as exploration and rescue by collaborative operation. This paper proposes a route planner based on a dynamic aggregation strategy to select the time-optimal routes for function-constrained UGV and fuel-constrained UAV to reach all the mission points. To reduce the time consumption of waiting at the take-off point, UGV and UAV are inclined to rendezvous at the next meeting point by no-wait operations. We present a two-step routing strategy to address this cooperative problem. First, the Voronoi diagram and greedy set cover method are used to determine meeting points and task areas. Then, solving a traveling salesman problem to find a UGV path by A-star and Lin-Kernighan heuristic. After that, an adaptive large neighborhood search heuristic is proposed to address vehicle routing problem with recharge for UAV among multiple task areas. The simulation and real experiment results demonstrate that our approach can effectively generate a time-optimal path for UGV-UAV Cooperative exploration.", "keywords": [ "Computer science", "Rendezvous", "Motion planning", "Heuristic", "Travelling salesman problem", "Search and rescue", "Task (project management)", "Routing (electronic design automation)", "Trajectory", "Robot", "Artificial intelligence", "Engineering" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icus55513.2022.9987158" }, { "id": "https://openalex.org/W4416875282", "doi": "10.1109/qce65121.2025.00229", "title": "QUADRO: A Hybrid Quantum Optimization Framework for Drone Delivery", "year": 2025, "publication_date": "2025-08-30", "type": "conference-paper", "language": null, "authors": [ "James B. Holliday", "Darren Blount", "Hoang Quan Nguyen", "Samee U. Khan", "Ky Luu" ], "first_author": "James B. Holliday", "venue": null, "publisher": null, "abstract": "Quantum computing holds transformative potential for optimizing large-scale drone fleet operations, yet its nearterm limitations necessitate hybrid approaches blending classical and quantum techniques. This work introduces Quantum Unmanned Aerial Delivery Routing Optimization (QUADRO), a novel hybrid framework addressing the Energy-Constrained Capacitated Unmanned Aerial Vehicle Routing Problem and the Unmanned Aerial Vehicle Scheduling Problem. By formulating these challenges as Quadratic Unconstrained Binary Optimization problems, QUADRO leverages the Quantum Approximate Optimization Algorithm for routing and scheduling, enhanced by classical heuristics and post-processing. We minimize total transit time in routing, considering payload and battery constraints, and optimize makespan scheduling across various drone fleets. Evaluated on adapted Augerat benchmarks (16-51 nodes), QUADRO competes against classical and prior hybrid methods, achieving scalable solutions with fewer than one hundred qubits. The proposed results underscore the viability of hybrid quantumclassical strategies for real-world drone logistics, paving the way for quantum-enhanced optimization in the Noisy Intermediate Scale Quantum era.", "keywords": [ "Drone", "Scheduling (production processes)", "Job shop scheduling", "Payload (computing)", "Heuristics", "Scalability", "Quantum" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/qce65121.2025.00229" }, { "id": "https://openalex.org/W4392346071", "doi": "10.7307/ptt.v36i1.261", "title": "Optimisation of Multi-Type Logistics UAV Scheduling under High Demand", "year": 2024, "publication_date": "2024-03-01", "type": "article", "language": "en", "authors": [ "Hong Jiang", "Tong Qing Wu", "Xinhui Ren", "Lizhen Gou" ], "first_author": "Hong Jiang", "venue": "PROMET - Traffic&Transportation", "publisher": "University of Zagreb, Faculty of Transport and Traffic Sciences", "abstract": "At present, interest in the application of unmanned aerial vehicles (UAV) for delivery is growing. A new “multi-type of UAV collaborative delivery” mode has been proposed. Through a combination of large, medium and small UAVs, the delivery capabilities of the UAV logistics system are significantly improved. Sometimes there is high demand, resulting in planned delivery routes that are no longer feasible, and even cause a shortage of distribution centre capacity and drones. This study explores logistics delivery strategies to solve problems caused by high demand. In this study, a multitype and multidistribution UAV model was established with the objective of minimising the total cost of distribution by considering factors such as the UAV energy consumption, load and distribution centre conditions. An improved ant colony algorithm was designed and its effectiveness was verified through the variability of the calculation time and multiple calculation results of different-scale examples. Finally, the classic vehicle routing problem (VRP) case is used in three scenarios to analyse the UAV scheduling optimisation problem. The results indicate that assisted delivery can reduce costs by 3% while ensuring delivery timeliness. The results of this study can provide guidance and benchmarks for the application of UAVs in urban logistics delivery systems.", "keywords": [ "Computer science", "Scheduling (production processes)", "Operations research", "Operations management", "Engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://traffic2.fpz.hr/index.php/PROMTT/article/download/261/119" }, { "id": "https://openalex.org/W3142989865", "doi": null, "title": "Studi Komparasi Penggunaan Drone untuk LogistikLast-mile", "year": 2020, "publication_date": "2020-10-05", "type": "article", "language": "id", "authors": [ "Rizky Gilang Pratama", "Evi Fortuna Alfaridzi", "Ahmar Aji Awaluddin", "Setyo Tri Windras Mara", "Achmad Pratama Rifai" ], "first_author": "Rizky Gilang Pratama", "venue": null, "publisher": null, "abstract": "Sejak beberapa perusahaan besar menerapkan drone untuk logistik last-mile, penelitian dengan topik terkait semakin banyak dilakukan. Penggunaan drone menjadi tren dalam riset logistik karena kapabilitasnya untuk melakukan proses delivery barang dengan jarak Euclidean dan durasi waktu yang relatif lebih cepat bila dibandingkan dengan truk ataupun kendaraan darat lainnya.\r\nInisiasi perkembangan sudah dilakukan diantaranya adalah konsep mengombinasikan drone dengan truk. Beberapa model tersebut diantaranya Flying Sidekick Traveling Salesman Problem (FSTSP), Parallel Drone Scheduling Traveling Salesman Problem (PDSTSP), Vehicle Routing Problem with Drones (VRPD), dan Multiple Flying Sidekick Traveling Salesman Problem (mFSTSP). Hanya saja, belum ada literatur yang melakukan studi komparasi untuk melihat model paling optimal untuk digunakan antar model tersebut dengan parameter delivery time. \r\nStudi komparasi tersebut untuk membandingkan hasil kalkulasi model TSP, FSTSP, dan PDSTSP. Model tersebut dibandingkan performanya menggunakan piranti lunak komersial GUROBI dan Python. Perbandingan dilakukan dengan menggunakan 15 dataset yang melingkupi beberapa titik koordinat customer di kota Yogyakarta. Dari hasil eksperimen, dapat dilihat bahwa secara keseluruhan FSTSP dan PDSTSP lebih efektif dibandingkan TSP berdasarkan total durasi penyelesaian misi pengiriman barang. Lebih jauh, FSTSP lebih unggul daripada PDSTSP saat jarak rata-rata customer lebih jauh, dan sebaliknya.", "keywords": [ "Computer science", "Drone", "Travelling salesman problem", "Humanities", "Algorithm", "Art", "Botany" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://repository.ugm.ac.id/276367/" }, { "id": "https://openalex.org/W7154570543", "doi": "10.3390/su18083978", "title": "Strategic Planning for Sustainable Last-Mile Logistics: Balancing Airspace Constraints and Carbon Price Uncertainty in Truck-Drone Delivery", "year": 2026, "publication_date": "2026-04-16", "type": "article", "language": "en", "authors": [ "Chengyou Cui", "J J Li" ], "first_author": "Chengyou Cui", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The accelerated growth of e-commerce has intensified the dual challenges of weak infrastructure and carbon emission pressures in last-mile delivery for rural and mountainous regions. As the World Bank calls for integrating carbon market development into national strategies, Truck-Drone Collaborative Delivery (TDCD) has emerged as a critical sustainable solution. However, existing research often overlooks the strict airspace regulations in sensitive border areas. Therefore, this paper proposes a Vehicle Routing Problem with Drones and Mobile Base Stations (VRPDBS) model that explicitly incorporates airspace constraints and mobile hub deployment. We introduce a quantified “Regional Flyability Factor” (fk) to measure the impact of airspace restrictions on routing decisions and solve the problem using a hybrid metaheuristic algorithm. A case study based on real-world data from the Yanbian Korean Autonomous Prefecture reveals that strict airspace compliance imposes an absolute delivery delay of 4–5 h and an operational cost premium of up to 15%, an impact that can be effectively mitigated through a mobile base station mediation strategy. More importantly, multi-scenario sensitivity analysis under carbon price uncertainty indicates that although truck-dominant modes are cost-effective at current low carbon prices, drone-intensive configurations demonstrate superior economic robustness and environmental performance under high carbon price scenarios. This study not only provides a technical framework for green logistics planning in complex airspace but also offers strategic decision support for logistics enterprises to navigate long-term climate policy risks.", "keywords": [ "Robustness (evolution)", "Greenhouse gas", "Robust optimization", "Carbon price", "Decision support system", "Matching (statistics)", "Strategic planning" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/18/8/3978/pdf?version=1776347601" }, { "id": "https://openalex.org/W4283733424", "doi": "10.1007/s11276-022-03031-8", "title": "COCOMA: a resource-optimized cooperative UAVs communication protocol for surveillance and monitoring applications", "year": 2022, "publication_date": "2022-06-30", "type": "article", "language": "en", "authors": [ "Youssra Cheriguene", "Fatima Zohra Bousbaa", "Chaker Abdelaziz Kerrache", "Soumia Djellikh", "Nasreddine Lagraa", "Mohamed Lahby", "Abderrahmane Lakas" ], "first_author": "Youssra Cheriguene", "venue": "Wireless Networks", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Multicast", "Swarm behaviour", "Distributed computing", "Computer network", "Routing protocol", "Routing (electronic design automation)", "Quality of service", "Transmission (telecommunications)", "Software deployment", "Telecommunications", "Artificial intelligence" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s11276-022-03031-8" }, { "id": "https://openalex.org/W2566818358", "doi": "10.1109/ssrr.2016.7784326", "title": "A column generation approach for optimized routing and coordination of a UAV fleet", "year": 2016, "publication_date": "2016-10-01", "type": "conference-paper", "language": "en", "authors": [ "Julia Zillies", "Stephan Westphal", "Dinesh Thakur", "Vijay Kumar", "George J. Pappas", "David H. Scheidt" ], "first_author": "Julia Zillies", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vecicles (UAVs) in civil and military applications are becoming increasingly popular. Various platform types have already shown their great potential in missions that require rapid surveillance capabilities or logistic support. Large scale incidents require the deployment of several platforms with various capabilities. In this case, coordinated use will lead to more efficient use of the given resources. Problems to resolve resemble known optimization problems from the field of vehicle routing or scheduling. The problem considered in this work includes a given team of homogenous UAVs and a set of target locations with certain requests that need to be served. It is modeled as a variant of the Vehicle Routing Problem (VRP) that is known to be NP hard, i.e. until now no algorithm is known that can solve the problem in polynomial run-time. In this paper, the problem is formulated using a path flow formulation and a column generation algorithm has been implemented and tested to solve simulated realtime instances of the problem in suitable time*.", "keywords": [ "Column generation", "Software deployment", "Computer science", "Vehicle routing problem", "Scheduling (production processes)", "Routing (electronic design automation)", "Distributed computing", "Optimization problem", "Set (abstract data type)", "Operations research", "Mathematical optimization", "Computer network" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ssrr.2016.7784326" }, { "id": "https://openalex.org/W3010525242", "doi": "10.1109/globecom38437.2019.9014091", "title": "Trajectory Planning for Multi-UAV Assisted Wireless Networks in Post-Disaster Scenario", "year": 2019, "publication_date": "2019-12-01", "type": "conference-paper", "language": "en", "authors": [ "Yu Lin", "Tianyu Wang", "Shaowei Wang" ], "first_author": "Yu Lin", "venue": null, "publisher": null, "abstract": "Recently, unmaned aerial vehicle (UAV) assisted wireless network has been recognized as a promising technology for post-disaster communications, in which multiple UAVs are dynamically deployed in the air as cellular base stations to provide public wireless connectivities when the ground infrastructure collapses. However, due to the non- uniformity of post-disaster traffic distribution and the large scale of post-disaster area, trajectory planning is considered as a major challenge for UAV- assisted post-disaster communications in many aspects including coverage, energy efficiency and computational complexity. In this paper, we consider a general trajectory planning problem for multi-UAV assisted wireless networks in a post-disaster scenario. We show that the problem can be formulated as a multi-depot vehicle routing problem. Then, we propose two heuristic algorithms that can efficiently utilize the battery of UAVs to improve the coverage performance. Simulation results show that, compared to the intuitive greedy algorithm, the coverage ratio can be improved by 8% and 28%, respectively, by using the proposed algorithms.", "keywords": [ "Computer science", "Base station", "Trajectory", "Heuristic", "Greedy algorithm", "Disaster area", "Wireless", "Wireless network", "Computer network", "Routing (electronic design automation)", "Emergency management", "Motion planning" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/globecom38437.2019.9014091" }, { "id": "https://openalex.org/W7131190485", "doi": "10.1016/j.ejor.2026.02.025", "title": "Modeling and planning a novel logistic transport system: Several drone pickups for each delivery", "year": 2026, "publication_date": "2026-02-23", "type": "article", "language": "en", "authors": [ "Javier E. Gómez-Lagos", "Benjamín Rojas-Espinoza", "Alfredo Candia-Véjar" ], "first_author": "Javier E. Gómez-Lagos", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "GRASP", "Metaheuristic", "Routing (electronic design automation)", "Integer programming", "Vehicle routing problem", "Linear programming" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2026.02.025" }, { "id": "https://openalex.org/W2809971438", "doi": null, "title": "Efficient Heuristic Algorithms for Drone Package Delivery Route", "year": 2016, "publication_date": "2016-01-01", "type": "article", "language": "en", "authors": [ "Yonatan Ayalew Kelkile", "Temesgen Seyoum", "Jai-Hoon Kim" ], "first_author": "Yonatan Ayalew Kelkile", "venue": null, "publisher": null, "abstract": "Drone package delivery routing problem is realistic problem used to find efficient route of drone package delivery service. In this paper, we present an approach for solving drone routing problem for package delivery service using two different heuristics algorithms, genetic and nearest neighbor. We...", "keywords": [ "Drone", "Heuristics", "Computer science", "Routing (electronic design automation)", "Heuristic", "Vehicle routing problem", "Service (business)", "Mathematical optimization", "Distributed computing", "Artificial intelligence", "Computer network", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NPAP12620695" }, { "id": "https://openalex.org/W2340613667", "doi": "10.1007/978-3-319-28555-9_3", "title": "Concept of Indoor 3D-Route UAV Scheduling System", "year": 2016, "publication_date": "2016-01-01", "type": "book-chapter", "language": "en", "authors": [ "Yohanes Khosiawan", "Izabela Ewa Nielsen", "Ngoc Anh Dung Do", "Bernardo Nugroho Yahya" ], "first_author": "Yohanes Khosiawan", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Computer science", "Scheduling (production processes)", "Real-time computing", "Distributed computing", "Schedule", "Motion planning", "Dynamic priority scheduling", "Two-level scheduling", "Artificial intelligence", "Robot", "Engineering", "Operations management" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-319-28555-9_3" }, { "id": "https://openalex.org/W4285151401", "doi": "10.1007/978-3-030-96935-6_27", "title": "Drones and Delivery Robots: Models and Applications to Last Mile Delivery", "year": 2022, "publication_date": "2022-01-01", "type": "book-chapter", "language": "en", "authors": [ "Cheng Chen", "Emrah Demir" ], "first_author": "Cheng Chen", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Last mile (transportation)", "Drone", "Vehicle routing problem", "Robot", "Mile", "Routing (electronic design automation)", "Transport engineering", "Value proposition", "Engineering", "Computer science", "Business", "Marketing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-96935-6_27" }, { "id": "https://openalex.org/W4220929974", "doi": "10.1080/13658816.2022.2045605", "title": "Optimizing UAV traffic monitoring routes during rush hours considering spatiotemporal variation of monitoring demand", "year": 2022, "publication_date": "2022-03-07", "type": "article", "language": "en", "authors": [ "Ke Wang", "Qianqian Wu", "Xiangting He", "Chuli Hu", "Nengcheng Chen" ], "first_author": "Ke Wang", "venue": "International Journal of Geographical Information Systems", "publisher": "Taylor & Francis", "abstract": "Dynamic changes in traffic conditions cause spatiotemporal variation in traffic monitoring demand. It is, therefore, necessary to conduct efficient road monitoring to identify dynamic abnormal situations, especially in peak traffic periods. Recently, unmanned aerial vehicles (UAVs) have become an attractive solution to this problem. However, UAV monitoring routes suffer from time limitations during peak traffic hours. To optimize UAV monitoring routes during rush hours, we develop a route planning method incorporating spatiotemporal variations in monitoring demand, in which we introduce a team orienteering arc routing problem with time-varying profits (TOARP-TP) and construct a corresponding mathematical model. The TOARP-TP is an extension of an already existing routing problem, team orienteering arc routing problem (TOARP). An iterated local search (ILS)-based algorithm is designed to solve the large instances of this problem. To verify the proposed method, we conduct sets of numerical experiments with the Sioux Falls road network in South Dakota, US, and a case study is applied using Wuhan, Hubei, PRC. The results demonstrate the efficiency and practicality of our method in optimizing UAV traffic monitoring routes during rush hours. Furthermore, we discuss a strategy for scenario determination and method selection in UAV route planning.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Orienteering", "Arc routing", "Real-time computing", "Route planning", "Operations research", "Transport engineering", "Geography", "Engineering", "Computer network" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/13658816.2022.2045605" }, { "id": "https://openalex.org/W1992897947", "doi": "10.5139/jksas.2010.38.5.427", "title": "Task Assignment of Multiple UAVs using MILP and GA", "year": 2010, "publication_date": "2010-05-01", "type": "article", "language": "en", "authors": [ "Hyun‐Jin Choi", "Joongbo Seo", "Youdan Kim" ], "first_author": "Hyun‐Jin Choi", "venue": "Journal of the Korean Society for Aeronautical & Space Sciences", "publisher": "Korean Society for Aeronautical and Space Science", "abstract": "본 논문은 다수의 목표물과 다수의 임무가 존재하는 상황에서의 다수 무인항공기의 임무할당 문제를 다룬다. 다수 무인항공기의 임무할당 문제는 순회 세일즈맨 문제, 차량 라우팅 문제와 같은 조합최적화 문제의 일종으로 NP-hard의 계산 복잡도를 가지고 있다. 이런 성격의 문제는 문제의 크기가 커질수록 계산시간이 급격히 증가하는 특징을 지니기 때문에 문제를 효율적으로 풀기 위해서 근사화 방법 또는 발견적인 방법을 사용한다. 본 연구에서는 임무할당 문제를 혼합정수 선형계획 문제로 정식화하고, 혼합정수 선형계획법과 유전 알고리듬으로 해를 구하였다. 다수의 목표물, 다수의 임무, 장애물이 존재하는 환경에 대한 수치 시뮬레이션을 수행하여 각 방법의 최적성과 효율성에 대해 검토하였다. This paper deals with a task assignment problem of multiple UAVs performing multiple tasks on multiple targets. The task assignment problem of multiple UAVs is a kind of combinatorial optimization problems such as traveling salesman problem or vehicle routing problem, and it has NP-hard computational complexity. Therefore, computation time increases as the size of considered problem increases. To solve the problem efficiently, approximation methods or heuristic methods are widely used. In this study, the problem is formulated as a mixed integer linear program, and is solved by a mixed integer linear programming and a genetic algorithm, respectively. Numerical simulations for the environment of the multiple targets, multiple tasks, and obstacles were performed to analyze the optimality and efficiency of each method.", "keywords": [ "Task (project management)", "Computer science", "Mathematical optimization", "Genetic algorithm", "Mathematics", "Engineering", "Systems engineering" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "http://koreascience.or.kr:80/article/JAKO201015233334023.pdf" }, { "id": "https://openalex.org/W4250484662", "doi": "10.1518/107118109x12524441079940", "title": "Effect of Level of Automation on Unmanned Aerial Vehicle Routing Task", "year": 2009, "publication_date": "2009-10-19", "type": "article", "language": "en", "authors": [ "Gloria L. Calhoun", "Mark H. Draper", "Heath A. Ruff" ], "first_author": "Gloria L. Calhoun", "venue": "Proceedings of the Human Factors and Ergonomics Society Annual Meeting", "publisher": "SAGE Publishing", "abstract": "Supervisory control of multiple unmanned aerial vehicles (UAVs) raises many issues concerning the balance of system autonomy with human interaction for optimal operator situation awareness and system performance. A UAV simulation environment designed to manipulate the application of automation was used to evaluate participants' performance on routing tasks under three levels of automation. Trials also involved completion of several mission-related secondary tasks as participants supervised either one or three UAVs. Both objective and subjective data were collected. The results showed that participants took longer to complete the routing task when automation was high due to the time they spent verifying the accuracy of the imperfect decision aid. These results show the importance of designing an interface that provides an efficient means of interacting with the automation and communicates the automation's rationale, especially under high automation levels.", "keywords": [ "Automation", "Task (project management)", "Routing (electronic design automation)", "Computer science", "Aeronautics", "Aerospace engineering", "Engineering", "Embedded system", "Systems engineering", "Mechanical engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1518/107118109x12524441079940" }, { "id": "https://openalex.org/W4412000940", "doi": "10.1016/j.dt.2025.06.025", "title": "Cross-platform mission planning for UAVs under carrier delivery mode", "year": 2025, "publication_date": "2025-07-03", "type": "article", "language": "en", "authors": [ "Junhong Jin", "Genlai Zhang", "Xin Yuan Li", "Xichao Su", "Chen Lu", "Yujie Cheng", "Yu Ding", "Lei Wang", "Xinwei Wang" ], "first_author": "Junhong Jin", "venue": "Defence Technology", "publisher": "Elsevier BV", "abstract": "As battlefield scale enlarges, cross-platform collaborative combat provides an appealing paradigm for modern warfare. Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning, where path planning and target assignment are tightly coupled. In this paper, we focus on UAV mission planning under carrier delivery mode (e.g., by aircraft carrier, ground vehicle, or transport aircraft) and design a three-layer hierarchical solution framework. In the first layer, we simultaneously determine delivery points and target set division by clustering. To address the safety concerns of radar risk and UAV endurance, an improved density peak clustering algorithm is developed by constraint fusion. In the second layer, mission planning within each cluster is viewed as a cooperative multiple-task assignment problem. A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy (VHSG) and a stochastic variable neighborhood search (SVNS), called VHSG-SVNS, is proposed for rapid solution. Based on the results of the first two layers, the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window. The cost between any two nodes is calculated by the A* algorithm, and the genetic algorithm is then implemented to determine the global route. Finally, a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework, where three layers cooperate well with each other to generate satisfactory combat scheduling. Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms. Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems, and can be potentially extended to other applications such as post-disaster search and rescue, forest surveillance and firefighting, logistics pick and delivery, etc.", "keywords": [ "Mode (computer interface)", "Systems engineering", "Computer science", "Aerospace engineering", "Engineering", "Aeronautics", "Human–computer interaction" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.dt.2025.06.025" }, { "id": "https://openalex.org/W4313595039", "doi": "10.1007/s40815-022-01435-4", "title": "Vessel-UAV Collaborative Optimization for the Offshore Oil and Gas Pipelines Inspection", "year": 2023, "publication_date": "2023-01-04", "type": "article", "language": "en", "authors": [ "Guiqin Xue", "Yantong Li", "Zheng Wang" ], "first_author": "Guiqin Xue", "venue": "International Journal of Fuzzy Systems", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Mathematical optimization", "Pipeline transport", "Tabu search", "Heuristic", "Pipeline (software)", "Fuzzy logic", "Computational intelligence", "Integer programming", "Genetic algorithm", "Routing (electronic design automation)", "Operations research" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s40815-022-01435-4" }, { "id": "https://openalex.org/W1510683507", "doi": "10.1109/acc.2015.7172161", "title": "Time-space network based exact models for periodical monitoring routing problem", "year": 2015, "publication_date": "2015-07-01", "type": "conference-paper", "language": "en", "authors": [ "Muhong Zhang", "Derek B. Kingston" ], "first_author": "Muhong Zhang", "venue": null, "publisher": null, "abstract": "Unmanned Air Vehicles (UAVs) have been used cooperatively with Unattended Ground Sensors (UGSs) to monitor and identify intruders on a road network system. The current practice includes four phases. In the patrol phase, UAVs visits UGSs periodically to detect the possible alarms. Once an alarm is detected, the second isolation phase is activated to determine the region of the intruders on the network. Delivery/ classification and response are the two following phases, which need more human involvements. This work focuses on the first patrol phase to determine the optimal patrol route visiting the UGSs periodically with minimal weighted revisit time interval. Since the objective focuses on a single UGS with worst performance (longest revisit time) and there are large number of alternative optimal solutions, a secondary objective is considered to minimize the duration of the total reiterative tour. Two mixed integer programming models are considered on a time-space discretized network. Numerical experiments show the nontrivial solutions on a small network. The second model improves the computational performance from the first model. The quality of the solutions are evaluated by upper and lower bounds.", "keywords": [ "Computer science", "Interval (graph theory)", "Integer programming", "Discretization", "Routing (electronic design automation)", "ALARM", "Real-time computing", "Duration (music)", "Phase (matter)", "Computer network", "Integer (computer science)", "Mathematical optimization" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/acc.2015.7172161" }, { "id": "https://openalex.org/W4388561877", "doi": "10.1109/dasc58513.2023.10311314", "title": "Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Hybrid Monte Carlo Tree Search Approach", "year": 2023, "publication_date": "2023-10-01", "type": "conference-paper", "language": "en", "authors": [ "Kenny Chour", "Priyank Pradeep", "Alexey A. Munishkin", "Krishna Kalyanam" ], "first_author": "Kenny Chour", "venue": null, "publisher": null, "abstract": "We present the Multi-Route Weighted Package Delivery Problem (MRWPDP) and a scalable solution methodology as a major step towards enabling an airspace deconfliction service for drone delivery operations. The problem is motivated by Strategic deconfliction under the FAA’s \"Unmanned Aircraft Systems Traffic Management\" Concept of Operations. MRWPDP falls under a class of vehicle routing and scheduling problems, and as such is NP-Hard. In MRWPDP, a graph network is given which consists of depots, drop-off sites, and multiple routes connecting the two. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is to optimally schedule the departure time and assign routes to a known set of vehicles at the depot. We propose a heuristic solution to the problem by borrowing techniques from Mixed Integer Linear Programming (MILP), Constraint Programming, and Monte Carlo Tree Search (MCTS). The resulting hybrid framework is MCTS with Bound-and-Prune (BP) and rapid simulated updates (U), or MCTS-BP-U. This approach is able to quickly provide a feasible solution for MRWPDP, even for large problem instances up to 1000 vehicles. We provide a MILP formulation of MRWPDP and compare its performance against MCTS-BP-U in terms of solution quality. An agent-based model simulation is conducted as a final step to validate the efficacy of our approach.", "keywords": [ "Computer science", "Monte Carlo method", "Scheduling (production processes)", "Monte Carlo tree search", "Vehicle routing problem", "Routing (electronic design automation)", "Mathematical optimization", "Computer network", "Mathematics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dasc58513.2023.10311314" }, { "id": "https://openalex.org/W7167852739", "doi": "10.1016/j.tre.2026.105074", "title": "Dynamic truck-drone collaborative routing with stochastic requests under changing weather conditions", "year": 2026, "publication_date": "2026-07-09", "type": "article", "language": "en", "authors": [ "Shan Zhao", "Jian Zhang", "Leandro Callegari Coelho" ], "first_author": "Shan Zhao", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Mathematical model", "Stochastic process", "Adaptive routing", "Vehicle routing problem" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105074" }, { "id": "https://openalex.org/W7142667653", "doi": "10.1016/j.tre.2026.104817", "title": "Last-mile delivery with drone resupply at transshipment platforms", "year": 2026, "publication_date": "2026-03-29", "type": "article", "language": "en", "authors": [ "Juan C. Pina-Pardo", "Matthias Winkenbach" ], "first_author": "Juan C. Pina-Pardo", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Transshipment (information security)", "Drone", "Truck", "Exploit", "TRIPS architecture", "Vehicle routing problem" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104817" }, { "id": "https://openalex.org/W4413104974", "doi": "10.1109/tem.2025.3597937", "title": "A Bi-Objective Truck Traveling Salesman Problem With Drone and Boat Considering an Equity-Based Perspective in Disaster Relief Distribution", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Fadillah Ramadhan", "Chandra Ade Irawan", "Antony Paulraj", "Cai Zhao" ], "first_author": "Fadillah Ramadhan", "venue": "IEEE Transactions on Engineering Management", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The distribution of disaster relief is a vital aspect of disaster response, which can reduce victim suffering during the post-disaster phase. Disaster relief distribution is a complex process, especially if available road access is damaged or blocked as a result of a disaster, such as a flood. Using trucks, drones, and in-flatable boats can solve this problem efficiently because drones and boats can access shelter locations that are difficult to serve by conventional vehicles like trucks. However, efficient distribution does not guarantee that the distribution process will be fair, considering that humanitarian logistics focuses more on social aspects, such as equity, which differs from other types of logistics. Therefore, this study aims to develop equity-based bi-objective models for the truck-drone-boat routing problem, referred to as the Bi-Objective Traveling Salesman Problem with Drone and Boat (BTSP-DB). Novel non-linear and linear models for the truck-droneboat routing problems are developed. This study also proposes a matheuristic-based method by combining an exact method and a heuristic, called an ϵ-Constraint with a Dynamic Hill Climbing-based method (EC-DHC) to solve the problem. The computational results of the case study and the generated datasets show that the method produces promising results, followed by providing valuable managerial implications for handling the flood disaster relief distribution problem", "keywords": [ "Travelling salesman problem", "Drone", "Perspective (graphical)", "Equity (law)", "Truck", "Distribution (mathematics)", "Operations research", "Aeronautics", "Computer science", "Transport engineering", "Engineering", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tem.2025.3597937" }, { "id": "https://openalex.org/W7134191807", "doi": "10.3390/drones10030184", "title": "Multi-Agent Deep Reinforcement Learning for Coding-Aware and Energy-Balanced Routing in Dynamic Drone Networks", "year": 2026, "publication_date": "2026-03-08", "type": "article", "language": "en", "authors": [ "Wu Yuhao", "Xiulin Qiu", "Bo Song", "Yaqi Ke", "Lei Xu", "Yuwang Yang" ], "first_author": "Wu Yuhao", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "By incorporating opportunistic coding, network throughput is enhanced, resulting in improved overall performance. However, applying this paradigm to Flying Ad-hoc Networks (FANETS) faces significant challenges due to the highly dynamic topology caused by the high-velocity mobility of UAVs, alongside the NP-hard complexity of identifying optimal coding opportunities in rapidly evolving aerial network architectures. To address these challenges, this paper proposes a novel coding-aware routing protocol based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). We formulate the routing problem as a multi-agent continuous decision-making process, employing the MADDPG algorithm to optimize routing policies in real-time through decentralized execution and centralized training. To maximize network utility, we design a comprehensive reward function that integrates coding benefits, throughput, energy distribution, and end-to-end delay, ensuring a balance between throughput maximization and the energy sustainability of individual UAV nodes. Simulation results demonstrate that the proposed protocol significantly outperforms state-of-the-art coding-aware routing protocols in terms of throughput, Packet Delivery Ratio (PDR), and transmission delay, exhibiting superior robustness in highly dynamic FANET scenarios. Notably, at a network density of 20 UAVs, MARL-CAR outperforms COPE, DCAR, TSCAR, and RLCAR in terms of coding ratio by 32.23%, 18.93%, 20.35%, and 5.5%, respectively. This research provides a scalable and intelligent networking solution for the next generation of autonomous UAV swarms and collaborative aerial missions.", "keywords": [ "Scalability", "Routing protocol", "Network packet", "Drone", "Linear network coding", "Robustness (evolution)", "Reinforcement learning", "Routing (electronic design automation)", "Dynamic Source Routing", "Network topology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/10/3/184/pdf?version=1773045935" }, { "id": "https://openalex.org/W2994544722", "doi": "10.1088/1757-899x/688/4/044004", "title": "A Research on Vehicle and UAV Routing Problem during Distribution Based on IAMMAS", "year": 2019, "publication_date": "2019-11-01", "type": "conference-paper", "language": "en", "authors": [ "Ming Han", "Yabin Wang", "Xiang Chen", "Xing Zhao" ], "first_author": "Ming Han", "venue": "IOP Conference Series Materials Science and Engineering", "publisher": "IOP Publishing", "abstract": "Abstract According to the characteristic of vehicle and unmanned aerial vehicle (UAV) in distribution service, a vehicle and UAV distribution mode were put forward in this paper to further expand the three-dimensional space of distribution, improve the efficiency of distribution and relieve the pressure of distribution personnel. Based on the consideration of real-time road condition, real-time driving speed, maximum driving distance and mixing time windows in distribution practice, a two-stage nested VRPTW model under vehicle and UAV distribution mode was constructed in this paper. A hybrid algorithm of genetic algorithm and adaptive max-min ant system algorithm (GA-AMMAS) was designed to solve the above problems. In addition, the actual road traffic conditions and vehicle actual travel time were added to the heuristic information in this algorithm. Data experiments shows that the vehicle and UAV routing problem can be effectively solved by GA-AMMAS. In addition, compared with GA and ACA, GA-AMMAS shows obvious advantages in quality solution and speed of convergence.", "keywords": [ "Vehicle routing problem", "Mode (computer interface)", "Convergence (economics)", "Genetic algorithm", "Computer science", "Heuristic", "Distribution (mathematics)", "Mathematical optimization", "Routing (electronic design automation)", "Ant colony optimization algorithms", "Algorithm", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://iopscience.iop.org/article/10.1088/1757-899X/688/4/044004/pdf" }, { "id": "https://openalex.org/W3125872024", "doi": "10.1109/infocomwkshps51825.2021.9484489", "title": "OPAR: Optimized Predictive and Adaptive Routing for Cooperative UAV Networks", "year": 2021, "publication_date": "2021-05-10", "type": "conference-paper", "language": "en", "authors": [ "M. Gharib", "Fatemeh Afghah", "Elizabeth Serena Bentley" ], "first_author": "M. Gharib", "venue": null, "publisher": null, "abstract": "Cooperative UAV networks are becoming increasingly popular in military and civilian applications. Alas, the typical ad-hoc routing protocols, which aim at finding the shortest path, lead to significant performance degradation because of the 3-dimension highly-dynamic nature of UAV networks and the uneven distribution of nodes across the network. This paper proposes OPAR, an optimized predictive and adaptive routing protocol, to face this challenging problem. We model the routing problem with linear programming (LP), where the goal is to maximize network performance, considering the path lifetime and path-length together. This model relies on a precise link lifetime prediction mechanism. We support the LP problem with a lightweight algorithm to find the optimized solution with a computation complexity of O(|E|2) , where |E| is the number of network links. We evaluate the OPAR performance and compare it with the well-known routing algorithms AODV, DSDV, and OLSR to cover a wide range of proactive and reactive protocols as well as distance vector and link-state techniques. We performed extensive simulations for different network densities and mobility patterns using the ns-3 simulator. Results show that OPAR prevents a high volume of routing traffic, increases the successful delivery by more than 30%, improves the throughput 25% on average, and decreases the flow completion time by an average of 35%1.", "keywords": [ "Computer science", "Destination-Sequenced Distance Vector routing", "Link-state routing protocol", "Distance-vector routing protocol", "Throughput", "Computer network", "Routing protocol", "Distributed computing", "Routing (electronic design automation)", "Wireless" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2101.06602" }, { "id": "https://openalex.org/W3202428833", "doi": "10.48550/arxiv.2103.04118", "title": "Towards Fully Autonomous Drone-Based Last-Mile Delivery", "year": 2021, "publication_date": "2021-03-06", "type": "preprint", "language": "en", "authors": [ "Navid Mohammad Imran", "Sabyasachee Mishra", "Myounggyu Won" ], "first_author": "Navid Mohammad Imran", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The drone-based last-mile delivery is an emerging technology to deliver parcels using drones loaded on a truck. As more and more autonomous vehicles (AVs) will be available for delivery services, an opportunity is arising to fully automate drone-based last-mile delivery. In this paper, we integrate AVs with drone-based last-mile delivery aiming to fully automate the last-mile delivery process. We define a new problem called the autonomous vehicle routing problem with drones (A-VRPD). A-VRPD is to select AVs from a pool of available AVs and to schedule them to serve customers with an objective of minimizing the total operational cost. We formulate A-VRPD as an Integer Linear Programming (ILP) and propose a greedy algorithm to solve the problem based on real-world operational costs for different types of AVs, traveling distances calculated considering the current traffic conditions, and varying load capacities of AVs. Extensive simulations performed under various random delivery scenarios demonstrate that the proposed algorithm effectively increases profits for both the delivery company and AV owners compared with traditional VRP-D (and TSP-D) algorithm-based approaches.", "keywords": [ "Drone", "Last mile (transportation)", "Mile", "Computer science", "Schedule", "Integer programming", "Vehicle routing problem", "Truck", "Linear programming", "Real-time computing", "Routing (electronic design automation)", "Process (computing)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2103.04118" }, { "id": "https://openalex.org/W4385981274", "doi": "10.1007/978-3-031-36118-0_56", "title": "Machine Learning for Unmanned Aerial Vehicle Routing on Rough Terrain", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Ievgen V. Sidenko", "A. S. Trukhov", "Galyna Kondratenko", "Yuriy Zhukov", "Yuriy Panteliyovych Kondratenko" ], "first_author": "Ievgen V. Sidenko", "venue": "Lecture notes on data engineering and communications technologies", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Terrain", "Routing (electronic design automation)", "Computer science", "Artificial neural network", "Artificial intelligence", "Real-time computing", "Task (project management)", "Engineering", "Simulation", "Embedded system", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-36118-0_56" }, { "id": "https://openalex.org/W3196007590", "doi": "10.1155/2021/4734023", "title": "Dynamic Routing and Coordination of Cluster for Unmanned Aerial Vehicle (UAV) Swarms", "year": 2021, "publication_date": "2021-08-23", "type": "article", "language": "en", "authors": [ "Ali Hashim Abbas", "Bhawani Shankar Chowdhry", "Muhammad Saqib", "Vishal Dattana" ], "first_author": "Ali Hashim Abbas", "venue": "Mathematical Problems in Engineering", "publisher": "Hindawi Publishing Corporation", "abstract": "The flying networks provide an efficient solution for a wide range of military and commercial purposes. The demand for portable and flexible communication is directed towards a quick growth in interaction among unmanned aerial vehicles (UAVs). Due to the frequent change in topology and high mobility of vehicles, routing and coordination becomes a challenging task. To maximize the throughput of the network, this study addresses the UAV swarm’s problems related to the coordination and routing and defines the proposed solution to solve these issues. For this, a network is assumed which contains an equal number of dynamic vehicles. It also presents the communication graph concept of UAVs and designs a fixed-wing UAV model to improve the efficiency of the network in terms of throughput. Furthermore, the proposed algorithm based on Cauchy particle swarm optimization (CPSO) aims towards the better performance of UAV swarms and aims to solve the combinatorial problem. The simulation results show and confirm the performance of the proposed algorithm.", "keywords": [ "Swarm behaviour", "Computer science", "Particle swarm optimization", "Throughput", "Routing (electronic design automation)", "Distributed computing", "Task (project management)", "Range (aeronautics)", "Drone", "Vehicle routing problem", "Real-time computing", "Topology (electrical circuits)" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://downloads.hindawi.com/journals/mpe/2021/4734023.pdf" }, { "id": "https://openalex.org/W4362583657", "doi": "10.58837/chula.the.2020.112", "title": "Lagrangian relaxation method for integrated vehicles and drones routing model", "year": 2020, "publication_date": "2020-01-01", "type": "dissertation", "language": "en", "authors": [ "Perawit Charoenwut", "Manoj Lohatepanont", "Shin-ei Takano" ], "first_author": "Perawit Charoenwut", "venue": null, "publisher": null, "abstract": "Nowadays, e-commerce increases parcel transportation demand year by year. Many companies are seeking the way to improve performance of delivery service and cost-efficiency. One among many solutions is using a drone attached with a truck for the last mile delivery. A drone can be launched from either depot or truck and returned to the depot or wait for the truck to take them back from the docking station around the delivery area. The vehicle routing problem with drones considers truck- and drone-route simultaneously. It is an enormous problem that cannot be solved by a normal linear programming method. This research proposes the Lagrangian relaxation technique, in conjunction with the Branch-and-Price technique, to estimate the lower bound of the solution in each iteration of the Branch-and-Price. The lower bound from the Lagrangian relaxation is tighter than the solution from the Column generation which can improve the total solution time. The bound use in Branch-and-Price as a fathom condition, these help the search tree not to explore unnecessary nodes. The computational experiment includes randomly generated instances and real-world instances. Moreover, the operation cost of truck-based and truck-drone-based are compared to point out the benefit of using drones.", "keywords": [ "Lagrangian relaxation", "Drone", "Truck", "Column generation", "Vehicle routing problem", "Branch and price", "Routing (electronic design automation)", "Computer science", "Branch and bound", "Lagrangian", "Upper and lower bounds", "Operations research" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://cuir.car.chula.ac.th/bitstream/123456789/77162/1/6170231921.pdf" }, { "id": "https://openalex.org/W4315605922", "doi": "10.1109/globecom48099.2022.10001484", "title": "Learning-Based Cooperative Aerial and Ground Vehicle Routing for Emergency Communications", "year": 2022, "publication_date": "2022-12-04", "type": "conference-paper", "language": "en", "authors": [ "Yuchao Zhu", "Shaowei Wang" ], "first_author": "Yuchao Zhu", "venue": "GLOBECOM 2022 - 2022 IEEE Global Communications Conference", "publisher": null, "abstract": "Unmanned aerial vehicle (UAV)-assisted communications has emerged as a promising technology in many domains, such as disaster relief and emergency scenarios. However, the limited battery capacity restricts UAVs from performing such persistent missions. In this paper, we consider a general hybrid trajectory planning problem to efficiently provide emergency communications in time-constrained disaster-affected regions, where a ground vehicle carrying backup batteries moves along with the UAV as a “mobile charging platform” to handle the energy issue of the UAV. Our optimization task is to minimize the total cost of the mission. We show that the optimization task is an extension of the traveling salesman problem with soft time window constraints. Due to the NP-hardness of the task, we propose a novel deep reinforcement learning with a sequential model strategy to learn the policy for the UAV's visiting order, based on which the collaborative routes of the UAV and ground vehicle are designed. Numerical results show that our proposed learning-based route planning scheme is effective and efficient.", "keywords": [ "Routing (electronic design automation)", "Computer science", "Telecommunications", "Transport engineering", "Computer network", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/globecom48099.2022.10001484" }, { "id": "https://openalex.org/W3025536926", "doi": "10.1007/s10846-020-01197-x", "title": "A Data-Driven Model for Evaluating the Survivability of Unmanned Aerial Vehicle Routes", "year": 2020, "publication_date": "2020-05-12", "type": "article", "language": "en", "authors": [ "Jun Guo", "Wei Xia", "Huawei Ma", "Xiaoxuan Hu" ], "first_author": "Jun Guo", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Survivability", "Computer science", "Aeronautics", "Engineering", "Aerospace engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10846-020-01197-x" }, { "id": "https://openalex.org/W4390670640", "doi": "10.3390/drones8010014", "title": "Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism", "year": 2024, "publication_date": "2024-01-07", "type": "article", "language": "en", "authors": [ "Shengqi Kang", "Xiuwen Fu" ], "first_author": "Shengqi Kang", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels.", "keywords": [ "Drone", "Computer science", "Benchmark (surveying)", "Sample (material)", "Blockchain", "Process (computing)", "Real-time computing", "Data mining", "Computer security", "Operating system" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/8/1/14/pdf?version=1704703130" }, { "id": "https://openalex.org/W4413993070", "doi": "10.3390/systems13090774", "title": "Drone-Assisted Order Picking Problem: Adaptive Genetic Algorithm", "year": 2025, "publication_date": "2025-09-04", "type": "article", "language": "en", "authors": [ "Esra Boz", "Erfan Babaee Tırkolaee" ], "first_author": "Esra Boz", "venue": "Systems", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This study tries to make some improvements in the order picking operations by offering a novel mathematical model and efficient solution algorithm. Accordingly, the order picking policies are examined to allow for picking more orders by reducing the collection time/distance of order pickers. Batching orders for the pick are included in the order picking process as it could enable the order picker to collect more orders. Since the most labor-intensive movement in the order picking function in a high-level shelf layout is the retrieval of products from upper shelves and placing them onto the collection vehicle in the picker-to-part system, the use of drones is preferred to eliminate this costly movement. Drones assist humans in the order picking process by retrieving products from upper levels, thus reducing the order picking time. Here, a Vehicle Routing Problem (VRP) is formulated to deal with drone routing which is then solved based on the Order Picking Problem (OPP) framework. Consequently, an integrated OPP involving both order pickers and drones is addressed and formulated using a Mixed-Integer Linear Programming (MILP) model. To cope with the complexity of the problem, an Adaptive Genetic Algorithm (AGA) is designed which is able to yield superior results compared to the classical Genetic Algorithm (GA). Finally, a sensitivity analysis is performed to assess the behavior of the model against real-world fluctuations. The main reason for this research is to speed up the order picking process in warehouses by taking advantage of the tools brought by the technology age. According to the research results, when the results of the drone-assisted order picking process are compared to the order picking process without drone support, an improvement of 29.68% is observed. The theoretical contribution of this work is that it initially mathematically defines the drone-aided OPP in the literature and proposes a solution with the help of the AGA. As a practical contribution, it provides a solution with the capacity to reduce operational costs by accelerating the order picking operation in warehouses and a practical optimization framework for logistics managers. In addition, warehouse managers, senior company managers, and researchers working on order picking processes can benefit from this study.", "keywords": [ "Drone", "Order (exchange)", "Computer science", "Genetic algorithm", "Algorithm", "Artificial intelligence", "Biology", "Machine learning", "Business", "Genetics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2079-8954/13/9/774/pdf?version=1756967758" }, { "id": "https://openalex.org/W4407485156", "doi": "10.1016/j.ejor.2025.02.007", "title": "A column generation-based matheuristic for an inventory-routing problem with driver-route consistency", "year": 2025, "publication_date": "2025-02-13", "type": "article", "language": "en", "authors": [ "Waleed Najy", "Claudia Archetti", "Ali H. Diabat" ], "first_author": "Waleed Najy", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Column generation", "Computer science", "Column (typography)", "Vehicle routing problem", "Consistency (knowledge bases)", "Routing (electronic design automation)", "Mathematical optimization", "Operations research", "Mathematics", "Computer network", "Artificial intelligence" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2025.02.007" }, { "id": "https://openalex.org/W4411799044", "doi": "10.1109/jiot.2025.3584277", "title": "Centralized Task Allocation for Multiple UAVs in Time-Constraint Industrial IoT Operations", "year": 2025, "publication_date": "2025-06-30", "type": "article", "language": "en", "authors": [ "Mohamad Abou Houran", "Gautam Srivastava", "Jawad Mirza", "Ali Ranjha", "Muhammad Awais Javed", "Muhammad Hamza Zafar" ], "first_author": "Mohamad Abou Houran", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The industrial Internet of Things (IoT) allows real-time monitoring and operational efficiency by enabling automated processes in industrial environments. In this paper, we propose a centralized task assignment framework for industrial IoT scenarios, focusing on light cargo delivery to specific locations via Unmanned Aerial Vehicles (UAVs). The task assignment problem is formulated as a Capacitated Vehicle Routing Problem (CVRP) with the objective of minimizing the maximum route distance among all UAVs. To solve CVRP, we employ a learning-based approach using an Attention Model (AM), which utilizes a deep learning framework with an encoder-decoder architecture to generate optimized UAV routes while satisfying the capacity constraints of UAVs. The AM is trained using policy gradient reinforcement learning to ensure that the solutions are both efficient and scalable. Numerical results demonstrate the effectiveness of the AM-based framework in delivering solutions that minimize the maximum tour length for deliveries.", "keywords": [ "Computer science", "Task (project management)", "Constraint (computer-aided design)", "Time constraint", "Internet of Things", "Resource allocation", "Computer network", "Distributed computing", "Real-time computing", "Computer security", "Engineering" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2025.3584277" }, { "id": "https://openalex.org/W2321076724", "doi": "10.2514/6.2011-1464", "title": "SMART Heuristic for Pickup and Delivery Problem (PDP) with Cooperative UAVs", "year": 2011, "publication_date": "2011-03-29", "type": "conference-paper", "language": "en", "authors": [ "Chelsea M. Sabo", "Kelly De Oliveira Cohen" ], "first_author": "Chelsea M. Sabo", "venue": "Infotech@Aerospace 2011", "publisher": null, "abstract": "Pickup and Delivery Problems (PDPs) are a subset of Vehicle Routing Problems (VRPs) which require a vehicle to service targets by picking them up at an origin and delivering them to their unique destination. With respect to surveillance functions, this becomes a realistic problem as UAVs are restricted by operating range, data rate, Anti-Jam margins, and cost. Therefore, UAVs must be allocated to “pickup” targets and then “deliver” them, from within a prescribed communication space, back to a command and control HQ. To maximize the speed/amount of information transmitted from this communication region, the objective of allocating the UAVs is such that the total service time (pickup and delivery) of all the targets is minimized. Previous work on PDPs has shown that as the problem gets more complicated (i.e. more targets and more vehicles) the solution space increases exponentially, and the execution time to find an optimal solution is impossible to implement. Additionally, previously related work using a heuristic solution has been applied to this problem showing that good result can be maintained (within ~1520% of the optimal solution for small cases). The focus of this research is to develop an alternate heuristic algorithm, deemed SMART from here on, that can perform near optimally (within ~5%) and scales as the problem gets more complicated. Also, the algorithm is developed such that it is easily extendable to a dynamic scenario as this research progresses. This algorithm is described in detail and has shown that it reaches this performance metric while requiring a significantly reduced computational time when compared to the time needed to obtain the optimal solution.", "keywords": [ "Pickup", "Computer science", "Heuristic", "Computer network", "Artificial intelligence" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2011-1464" }, { "id": "https://openalex.org/W4399801044", "doi": "10.1109/icuas60882.2024.10556920", "title": "A Multi-UAV Route Planning Method for Fast Inspection of Electric Power Transmission Lines", "year": 2024, "publication_date": "2024-06-04", "type": "conference-paper", "language": "en", "authors": [ "Alvaro Caballero", "Francisco Javier Roman-Escorza", "Iván Maza", "Anı́bal Ollero" ], "first_author": "Alvaro Caballero", "venue": null, "publisher": null, "abstract": "Electric power transmission lines are essential assets, and their maintenance is crucial to ensure a reliable service to society. However, with thousands of kilometers along the geography of any country, their inspection is costly and time-consuming. This paper proposes a route planning method for the fast inspection of power grids using a heterogeneous team of UAVs (Unmanned Aerial Vehicles). Given a power grid, the planning method, formulated as a vehicle routing problem, minimizes the mission time that the multi-UAV team needs to visually inspect the entire grid. The method integrates models of energy consumption and considers the existence of recharging stations where the aerial robots can recharge their batteries for long-term operation. Moreover, a clustering strategy is also presented to reduce the computational burden of the planning problem without compromising the quality of the solutions. The performance of the resulting approach is analyzed in simulation under different conditions that show the benefits. Finally, experimental results in the Doñana National Park (Spain) showcase the validity of the planning method for real-world applications.", "keywords": [ "Electric power transmission", "Computer science", "Transmission (telecommunications)", "Power (physics)", "Power transmission", "Electric power", "Electrical engineering", "Engineering", "Telecommunications", "Physics" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icuas60882.2024.10556920" }, { "id": "https://openalex.org/W3183781841", "doi": "10.48550/arxiv.2107.13275", "title": "Benchmark Instances and Optimal Solutions for the Traveling Salesman Problem with Drone", "year": 2021, "publication_date": "2021-07-28", "type": "preprint", "language": "en", "authors": [ "Mauro Dell’Amico", "Roberto Montemanni", "Stefano Novellani" ], "first_author": "Mauro Dell’Amico", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The use of drones in logistics is gaining more and more interest, and drones are becoming a more viable and common way of distributing parcels in an urban environment. As a consequence, there is a flourishing production of articles in the field of operational optimization of the combined use of trucks and drones for fulfilling customers requests. The aim is minimizing the total time required to service all the customers, since this has obvious economical impacts. However in the literature there is not yet a widely recognized basic model, and there are not well assessed sets of instances and optimal solutions that can be considered as a benchmark to prove the effectiveness of new solution methods. The aim of this paper is to fill this gap. On one side we will clearly describe some of the most common components of the truck/drone routing problems and we will define nine basic problem settings, by combining these components. On the other side we will consider some of the instances used by many researchers and we will provide optimal solutions for all the problem settings previously identified. Instances and detailed solutions are then organized into benchmarks made publicly available as validation tools for future research methods.", "keywords": [ "Drone", "Benchmark (surveying)", "Travelling salesman problem", "Computer science", "Truck", "Vehicle routing problem", "Operations research", "Field (mathematics)", "Service (business)", "Routing (electronic design automation)", "Flourishing", "Mathematical optimization" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2107.13275" }, { "id": "https://openalex.org/W4388936699", "doi": "10.1109/access.2023.3335932", "title": "Total Energy Cost Optimization for Data Collection With Boat-Assisted Drone: A Study on Large-Scale Marine Sensor", "year": 2023, "publication_date": "2023-01-01", "type": "article", "language": "en", "authors": [ "Xianfei Huang", "Gaocai Wang" ], "first_author": "Xianfei Huang", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "To handle ocean health problems associated with human activities (wind turbine deployment, nuclear wastewater discharge, etc.), collecting broad ocean region data to leverage large-scale sensors is a valid method. When using the novel collection scheme, reducing the total energy consumption (TEC) of boat-assisted drones to collect sensor data is challenging. The objective of this study was to minimize the TEC of boats and drones (owing to the limited battery capacity) during marine environment sensor data collection. To achieve this, new models for drone hovering in wind, data collection, and related wireless communication have been developed, and the TEC minimization problem has been formulated as a new specialized distance-constrained capacitated vehicle routing problem. The problem is divided into four sub-problems to reduce complexity. Based on these four subproblems, an improved heuristic algorithm is proposed. In the algorithm, the drone hovering position and boat waypoint are found using the K-means clustering algorithm and the smallest enclosing circle algorithm. Based on the position and waypoint, the routes of drones and boats are optimized using the Lin-Kernighan heuristic 3 algorithm, thus minimizing the TEC. The simulation results demonstrate that when the boat waypoint is 3 and the sensor number is 2000, owing to the strong local and global search ability, the TEC in the scheme is 0.03×108J less than that of the graph attention neural network method (GANN), while the scheme also provides time saving, scalability, and flexibility.", "keywords": [ "Waypoint", "Drone", "Computer science", "Real-time computing", "Wireless sensor network", "TEC", "Data collection", "Cluster analysis", "Algorithm", "Simulation", "Artificial intelligence", "Mathematics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/6287639/6514899/10327706.pdf" }, { "id": "https://openalex.org/W4362696338", "doi": "10.3233/jifs-224307", "title": "A hybrid large-neighborhood search for a truck and drone delivery system with stochastic customer existence and time windows", "year": 2023, "publication_date": "2023-04-07", "type": "article", "language": "en", "authors": [ "Ebrahim Teimoury", "Reza Rashid" ], "first_author": "Ebrahim Teimoury", "venue": "Journal of Intelligent & Fuzzy Systems", "publisher": "IOS Press", "abstract": "In recent years, e-commerce has become increasingly popular, and consumers expect quick and affordable delivery, placing additional pressure on city logistics activities. An innovative approach is proposed to coordinate ground vehicles and drones for delivery services, which has gained tremendous attention from academia and logistic service providers. This paper introduces a variant of this problem: the two-echelon truck and drone routing problem, characterized by stochastic demand existence and soft time windows. A Markov chain is used to model the problem, and a linear mathematical model is presented. This work employs a hybrid large-neighborhood search approach. Numerous computational experiments are conducted to evaluate the performance of the proposed solution method, and the results demonstrate its efficacy.", "keywords": [ "Drone", "Truck", "Computer science", "Markov chain", "Vehicle routing problem", "Service (business)", "Operations research", "Mathematical optimization", "Routing (electronic design automation)", "Engineering", "Mathematics", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3233/jifs-224307" }, { "id": "https://openalex.org/W4404689406", "doi": "10.1109/icds62089.2024.10756461", "title": "Mixed Electric Vehicles and UAVs for Last-Mile Delivery Under Ecological Objective", "year": 2024, "publication_date": "2024-10-23", "type": "conference-paper", "language": "en", "authors": [ "Bouhia Hanaa", "Jamal Benhra" ], "first_author": "Bouhia Hanaa", "venue": null, "publisher": null, "abstract": "The article introduces the Electric Vehicle Routing Problem with Drones (EVRPD), an innovative approach to optimizing last-mile delivery by integrating Electric Vehicles (EVs) with drones. The main goal is to minimize energy consumption and CO2 emissions while ensuring efficient delivery operations. This solution utilizes a mixed fleet of EVs as mother ships and drones for the final parcel delivery. EVs and drones are both capable of delivering parcels, with drones deployed for smaller and more urgent deliveries. The system focuses on optimizing the payload distribution to maximize energy efficiency and drone autonomy. Drones are replenished on the rooftops of EVs using IoT technology. To solve the routing problem, this solution employs bio-inspired algorithms such as Ant Colony Optimization (ACO) for EVs and Particle Swarm Optimization (PSO) for drones. This approach maximizes energy efficiency and drone autonomy while ensuring efficient parcel distribution. By integrating these innovative techniques, EVRPD offers promising prospects for a more sustainable and efficient urban logistics system.", "keywords": [ "Mile", "Last mile (transportation)", "Computer science", "Environmental science", "Ecology", "Transport engineering", "Geography", "Engineering", "Biology" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icds62089.2024.10756461" }, { "id": "https://openalex.org/W3196702647", "doi": "10.1016/j.procs.2021.07.031", "title": "Autonomous task allocation for multi-UAV systems based on area-restricted search behavior in animals", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Faten Aljalaud", "Heba A. Kurdi" ], "first_author": "Faten Aljalaud", "venue": "Procedia Computer Science", "publisher": "Elsevier BV", "abstract": "One of the main challenges in multi-unmanned aerial vehicle search and rescue missions is to efficiently distribute the search and rescue tasks over several unmanned aerial vehicles. Rescuing survivors quickly is critical since survivors have a limited life span. In this paper, we present a multi-unmanned aerial vehicle task allocation system inspired by area-restricted search, a foraging behavior of animals in which they focus on a local foraging area after finding prey in order to increase prey encounters. The algorithm’s performance was assessed in comparison to opportunistic coordination schemes. Simulation experiments indicated noticeable improvement in the mean rescue time. Also, the algorithm demonstrated better net throughput than the baseline performance, especially in small fleet sizes, without compromising runtime performance.", "keywords": [ "Computer science", "Foraging", "Search and rescue", "Task (project management)", "Baseline (sea)", "Real-time computing", "Focus (optics)", "Throughput", "Simulation", "Distributed computing", "Artificial intelligence", "Telecommunications" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S1877050921014265/pdf" }, { "id": "https://openalex.org/W2967184883", "doi": "10.1109/icuas.2019.8798167", "title": "Efficient Computation of Optimal UAV Routes for Persistent Monitoring of Targets", "year": 2019, "publication_date": "2019-06-01", "type": "conference-paper", "language": "en", "authors": [ "Sai Krishna Kanth Hari", "Sivakumar Rathinam", "Swaroop Darbha", "Krishna Kalyanam", "Satyanarayana G. Manyam", "David W. Casbeer" ], "first_author": "Sai Krishna Kanth Hari", "venue": null, "publisher": null, "abstract": "In this article, we consider a routing problem that arises in the scenario of persistently monitoring a set of targets using an unmanned vehicle. A UAV is tasked with monitoring a set of targets, by frequently visiting them for data collection. The UAV has a limited fuel capacity, which is specified in terms of the number of visits it makes, at the end of which it must be recharged at a depot. The problem considered here is to plan an optimal sequence of visits (to targets) for the UAV, such that the maximum time between consecutive revisits to the targets is minimized. The problem is a generalization of the Traveling Salesman Problem (TSP) and is computationally challenging; It is significantly difficult to compute optimal solutions using standard formulations. However, the authors recently developed a set of theoretical results characterizing the structure of optimal solutions for this problem. Using these results, in this article, we propose a new formulation to solve the problem. Extensive numerical simulations suggest that the average computation time required to solve the problem using the proposed formulation is within a fraction of a second on a standard laptop (here, a MacBook Pro with Intel Core i7 processor and 16 GB RAM was used).", "keywords": [ "Travelling salesman problem", "Computer science", "Computation", "Set (abstract data type)", "Generalization", "Laptop", "Mathematical optimization", "Sequence (biology)", "Fraction (chemistry)", "Algorithm", "Mathematics" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icuas.2019.8798167" }, { "id": "https://openalex.org/W4392659665", "doi": "10.3390/app14062365", "title": "Research on Multi-UAV Task Assignment Based on a Multi-Objective, Improved Brainstorming Optimization Algorithm", "year": 2024, "publication_date": "2024-03-11", "type": "article", "language": "en", "authors": [ "Xiaofang Wang", "Shi Yin", "Lianyong Luo", "Xin Qiao" ], "first_author": "Xiaofang Wang", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTLZ4, DTLZ5, and DTLZ6 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and IGD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters.", "keywords": [ "Brainstorming", "Computer science", "Task (project management)", "Optimization algorithm", "Mathematical optimization", "Artificial intelligence", "Engineering", "Mathematics", "Systems engineering" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/14/6/2365/pdf?version=1710168054" }, { "id": "https://openalex.org/W4294690771", "doi": "10.23919/acc53348.2022.9867385", "title": "Power Management for Noise Aware Path Planning of Hybrid UAVs", "year": 2022, "publication_date": "2022-06-08", "type": "conference-paper", "language": "en", "authors": [ "Drew D. Scott", "Satyanarayana G. Manyam", "David W. Casbeer", "Manish Kumar", "Michael J. Rothenberger", "Isaac E. Weintraub" ], "first_author": "Drew D. Scott", "venue": "2022 American Control Conference (ACC)", "publisher": null, "abstract": "Here we consider the problem of path planning for a hybrid fuel Unmanned Aerial Vehicle (UAV), equipped with both a battery and gasoline generator as two sources of energy. Routing the vehicle while considering this setup adds fuel constraints in addition to requiring the power source state to be specified along each edge along the path. We further add noise restrictions to the problem where certain edges (corresponding to certain portions of the path) have the restriction that the gasoline generator cannot be run. A solution to this path planning problem entails both a sequence of nodes, being the plain path the vehicle travels, and a sequence of values specifying the power source along each edge in the path. There is no work in the current literature regarding such a problem, with little at all concerning general planning or routing for hybrid fuel UAVs. Here, hybrid fuel UAV routing problem with noise restrictions is presented. The problem is first formulated as an Mixed Integer Linear Program. Then a dynamic programming algorithm is presented to solve the problem exactly. This algorithm is then compared to a branch-and-cut based method in terms of computational times for a variety of problem sizes. A case study is presented, using a graph which location data from an area surrounding an airport to produce goal nodes and noise restrictions to display the problem itself as well as the performance of the dynamic programming algorithm in a realistic scenario for this problem.", "keywords": [ "Computer science", "Mathematical optimization", "Motion planning", "Routing (electronic design automation)", "Path (computing)", "Generator (circuit theory)", "Integer programming", "Noise (video)", "Any-angle path planning", "Enhanced Data Rates for GSM Evolution", "Power (physics)", "Algorithm" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/acc53348.2022.9867385" }, { "id": "https://openalex.org/W3185525848", "doi": "10.48550/arxiv.2107.13672", "title": "Locating and Platforms and Scheduling a Fleet of Drones for Emergency Delivery of Perishable Items", "year": 2021, "publication_date": "2021-07-28", "type": "preprint", "language": "en", "authors": [ "Monica Gentili", "Pitu B. Mirchandani", "Alessandro Agnetis", "Zabih Ghelichi" ], "first_author": "Monica Gentili", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Motivated by issues dealing with delivery of emergency medical products during humanitarian disasters, this paper addresses the general problem of delivering perishable items to remote demands accessible only by helicopters or drones. Each drone operates out of platforms that may be move when not in use and each drone has a limited delivery range to service a demand points. Associated with each demand point is a disutility function, or a cost function, with respect to time that reflects preferred delivery clock time for the demanded item, as well as the item's perishability characteristic that models nonincreasing quality with time. The paper first addresses the problem of locating the platforms as well concurrently determining which platform serves which demand points and in what order to minimize total disutility for product delivery. The second scenario addresses the two-period problem where the platforms can be relocated, using useable road networks, after the first period. It can be easily proven that continuous time versions of these problems are NP-Hard. However, a practical \"time-slot\" version of the problem, where time is discretized into slots, can be solved by standard optimization software. Extensive computational experiments, using different drone delivery ranges as well as different drone fleet sizes, provide valuable insights on the performance of such drone delivery systems.", "keywords": [ "Drone", "Computer science", "Perishability", "Operations research", "Scheduling (production processes)", "Function (biology)", "Real-time computing", "Operations management", "Engineering", "Business", "Marketing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2107.13672" }, { "id": "https://openalex.org/W302903404", "doi": "10.1007/978-3-319-15892-1_16", "title": "Genetic Algorithm Approach for a Class of Multi-Criteria, Multi-Vehicle Planner of UAVs", "year": 2015, "publication_date": "2015-01-01", "type": "conference-paper", "language": "en", "authors": [ "Emory Raphael Viana Freitas", "José Reginaldo Hughes Carvalho" ], "first_author": "Emory Raphael Viana Freitas", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Genetic algorithm", "Intersection (aeronautics)", "Heading (navigation)", "Motion planning", "Turning radius", "Mathematical optimization", "Point (geometry)", "Vehicle routing problem", "Path (computing)", "Routing (electronic design automation)", "Planner" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-319-15892-1_16" }, { "id": "https://openalex.org/W2996171288", "doi": null, "title": "Analysis of supply chain operational performances using vehicle routing with UAV delivery in city logistics", "year": 2019, "publication_date": "2019-09-02", "type": "article", "language": "en", "authors": [ "Alexey Solomasov" ], "first_author": "Alexey Solomasov", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Vehicle routing problem", "Supply chain", "Routing (electronic design automation)", "Transport engineering", "Computer science", "Business", "Operations management", "Operations research", "Automotive engineering", "Engineering", "Computer network", "Marketing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://matheo.uliege.be/bitstream/2268.2/8463/4/Solomasov_MT_Analysis_SCOP_UAV.pdf" }, { "id": "https://openalex.org/W4288804341", "doi": "10.48550/arxiv.2207.13940", "title": "Very large-scale neighborhood search for drone routing with energy replenishment", "year": 2022, "publication_date": "2022-07-28", "type": "preprint", "language": "en", "authors": [ "Catherine Lorenz", "Nicola Mimmo", "Alena Otto", "Daniele Vigo" ], "first_author": "Catherine Lorenz", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The Drone Routing Problem with Energy replenishment (DRP-E) belongs to a general class of routing problems with intermediate stops and synchronization constraints. In DRP-E, the drone has to visit a set of nodes and routinely requires battery swaps from a (potentially) mobile replenishment station. Contrary to widespread restrictions in the drone routing literature, several destinations may be visited in between two consecutive battery swaps. In this paper, we propose a nontrivial very large-scale neighbourhood for DRP-E, which synergetically leverages two large-sized polynomially solvable DRP-E SubProblems (SP1 and SP2). The number of feasible solutions in the resulting neighborhood is a multiple of those in SP1 and SP2, and, thus, exponential in the input size of the problem, whereas the computational time to search it remains polynomial. The proposed polynomial two-stage dynamic programming algorithm VLSN to search this neighborhood can be flexibly adjusted to the desired trade-off between accuracy and computational time. For instance, the search procedure can be converted into an exact algorithm of competitive runtime for DRP-E. In computational tests, the developed solution methods outperform current state-of-the art heuristics for DRP-E by a significant margin. A case study based on a search for missing persons demonstrates that VLSN easily accommodates additional practice relevant features and outperforms the state-of-the-art solution in disaster relief by 20%.", "keywords": [ "Heuristics", "Computer science", "Drone", "Mathematical optimization", "Set (abstract data type)", "Routing (electronic design automation)", "Travelling salesman problem", "Cluster analysis", "Arc routing", "Class (philosophy)", "Algorithm", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2207.13940" }, { "id": "https://openalex.org/W4409463060", "doi": "10.1007/978-3-031-78918-2_24", "title": "Optimizing the TSP-D with Pity Beetle Algorithm to Minimize the Delivery Completion Time for UAV Routing Problems", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Yugeswary Kanesen", "Hasneeza Liza Zakaria", "M. S. Asi", "Rozmie Razif Othman", "Wan Nur Suryani Firuz Wan Ariffin", "Nuraminah Ramli", "Mohd Alif Hasmani Abd Ghani" ], "first_author": "Yugeswary Kanesen", "venue": "Studies in computational intelligence", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Pity", "Computer science", "Mathematical optimization", "Routing (electronic design automation)", "Routing algorithm", "Algorithm", "Mathematics", "Computer network", "Psychology", "Routing protocol" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-78918-2_24" }, { "id": "https://openalex.org/W4408559999", "doi": "10.1016/j.trpro.2025.02.005", "title": "Urban case studies on optimized truck-drone last-mile distribution", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Rui Zhang", "Junfeng Xiao", "Florian M. Heinitz" ], "first_author": "Rui Zhang", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "Evaluating an urban/sub-urban freight transport system with targeted drone sorties from and in addition to delivery trucks at the micro level, we present a computational application of a single-truck, multiple-drones routing problem with self-developed heuristics for a set of real urban networks using a program solver. Results from five cities worldwide demonstrate by example that the new approach – compared to optimum road-based delivery – is more carbon and cost efficient, as it can save 43 to 51% in delivery time at a scale of 20 customer nodes. Further to this, a quantitative analysis of the impact of customer node aggregation and varying drone-to-truck speed ratios on the profitability and negative externalities of the collaborative delivery system is posed. In effect, the solution quality increases at a lower computational effort.", "keywords": [ "Drone", "Mile", "Truck", "Transport engineering", "Last mile (transportation)", "Aeronautics", "Engineering", "Environmental science", "Computer science", "Geography", "Automotive engineering", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2025.02.005" }, { "id": "https://openalex.org/W4413212920", "doi": "10.1109/tcomm.2025.3597655", "title": "Trusted Routing for Blockchain-Empowered UAV Networks via Multi-Agent Deep Reinforcement Learning", "year": 2025, "publication_date": "2025-08-11", "type": "article", "language": "en", "authors": [ "Ziye Jia", "Sijie He", "Qiuming Zhu", "Wei Wang", "Qihui Wu", "Zhu Han" ], "first_author": "Ziye Jia", "venue": "IEEE Transactions on Communications", "publisher": "IEEE Communications Society", "abstract": "Due to the high flexibility and versatility, uncrewed aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue. However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we characterize the routing process in a time-varying UAV network with malicious nodes. Specifically, we formulate the routing problem to minimize the total delay, which is an integer linear programming and intractable to solve. Then, to tackle the network security issue, a blockchain-based trust management mechanism (BTMM) is designed to dynamically evaluate trust values and identify low-trust UAVs. To improve traditional practical Byzantine fault tolerance algorithms in the blockchain, we propose a consensus UAV update mechanism. Besides, considering the local observability, the routing problem is reformulated into a decentralized partially observable Markov decision process. Further, a multi-agent double deep Q-network based routing algorithm is designed to minimize the total delay. Finally, simulations are conducted with attacked UAVs and numerical results show that the delay of the proposed mechanism decreases by 13.39%, 12.74%, and 16.6% than multi-agent proximal policy optimal algorithms, multi-agent deep Q-network algorithms, and methods without BTMM, respectively.", "keywords": [ "Blockchain", "Reinforcement learning", "Computer science", "Routing (electronic design automation)", "Computer network", "Multi-agent system", "Distributed computing", "Artificial intelligence", "Computer security" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tcomm.2025.3597655" }, { "id": "https://openalex.org/W7160895660", "doi": "10.1016/j.tre.2026.104910", "title": "Dispatch-sequencing optimization for drone delivery services: Models, properties and cutting planes", "year": 2026, "publication_date": "2026-05-12", "type": "article", "language": "en", "authors": [ "Maohua Cai", "Zhiyu Xiang", "Yating Feng", "Kexin Cao", "Xinglu Liu", "Yandong He" ], "first_author": "Maohua Cai", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Speedup", "Sizing", "Throughput", "Set (abstract data type)", "Dynamic programming", "Range (aeronautics)", "Optimization problem" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104910" }, { "id": "https://openalex.org/W3107832326", "doi": "10.1007/978-3-030-63007-2_15", "title": "Declarative UAVs Fleet Mission Planning: A Dynamic VRP Approach", "year": 2020, "publication_date": "2020-01-01", "type": "conference-paper", "language": "en", "authors": [ "Grzegorz Radzki", "Peter Nielsen", "Amila Thibbotuwawa", "Grzegorz Bocewicz", "Zbigniew Antoni Banaszak" ], "first_author": "Grzegorz Radzki", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Operations research", "Collision avoidance", "Service (business)", "Runway", "Taxis", "Maximization", "Real-time computing", "Computer network", "Mathematical optimization" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-63007-2_15" }, { "id": "https://openalex.org/W2066238546", "doi": "10.1016/j.cor.2012.02.010", "title": "Simultaneous sensor selection and routing of unmanned aerial vehicles for complex mission plans", "year": 2012, "publication_date": "2012-02-18", "type": "article", "language": "en", "authors": [ "Frank Mufalli", "Rajan Batta", "Rakesh Nagi" ], "first_author": "Frank Mufalli", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Heuristics", "Computer science", "Column generation", "Routing (electronic design automation)", "Set (abstract data type)", "Task (project management)", "Sizing", "Generalization", "Real-time computing", "Operations research", "Mathematical optimization", "Systems engineering" ], "cited_by_count": 115, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2012.02.010" }, { "id": "https://openalex.org/W2952497442", "doi": null, "title": "Multi-UAV Routing for Persistent Intelligence Surveillance & Reconnaissance Missions", "year": 2017, "publication_date": "2017-02-27", "type": "preprint", "language": "en", "authors": [ "Satyanarayana G. Manyam", "Steven A. Rasmussen", "David W. Casbeer", "Krishna Kalyanam", "Suresh Manickam" ], "first_author": "Satyanarayana G. Manyam", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We consider a Persistent Intelligence, Surveillance and Reconnaissance (PISR) routing problem, which includes collecting data from a set of specified task locations and delivering that data to a control station. Each task is assigned a refresh rate based on its priority, where higher priority tasks require higher refresh rates. The UAV team's objective is to minimize the maximum of the delivery times of all the tasks' data to the control station, while simultaneously, satisfying each task's revisit period constraint. The centralized path planning problem for this PISR routing problem is formulated using mixed integer linear programming and solved using a branch-and-cut algorithm. Heuristics are presented to find sub-optimal feasible solutions that require much less computation time. The algorithms are tested on several instances and their performance is compared with respect to the optimal cost and computation time.", "keywords": [ "Heuristics", "Computer science", "Task (project management)", "Routing (electronic design automation)", "Integer programming", "Set (abstract data type)", "Real-time computing", "Computation", "Control (management)", "Constraint (computer-aided design)", "Linear programming", "Mathematical optimization" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1702.08494" }, { "id": "https://openalex.org/W1974311464", "doi": "10.1109/tase.2013.2279544", "title": "Algorithms for Routing an Unmanned Aerial Vehicle in the Presence of Refueling Depots", "year": 2013, "publication_date": "2013-10-03", "type": "article", "language": "en", "authors": [ "Kaarthik Sundar", "Sivakumar Rathinam" ], "first_author": "Kaarthik Sundar", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "We consider a single Unmanned Aerial Vehicle (UAV) routing problem where there are multiple depots and the vehicle is allowed to refuel at any depot. The objective of the problem is to find a path for the UAV such that each target is visited at least once by the vehicle, the fuel constraint is never violated along the path for the UAV, and the total fuel required by the UAV is a minimum. We develop an approximation algorithm for the problem, and propose fast construction and improvement heuristics to solve the same. Computational results show that solutions whose costs are on an average within 1.4% of the optimum can be obtained relatively fast for the problem involving five depots and 25 targets.", "keywords": [ "Heuristics", "Vehicle routing problem", "Constraint (computer-aided design)", "Computer science", "Routing (electronic design automation)", "Mathematical optimization", "Path (computing)", "Engineering", "Mathematics", "Computer network" ], "cited_by_count": 234, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1304.0494" }, { "id": "https://openalex.org/W3007668582", "doi": "10.1109/wsc40007.2019.9004655", "title": "A Simheuristic for the Unmanned Aerial Vehicle Surveillance-Routing Problem with Stochastic Travel Times and Reliability Considerations", "year": 2019, "publication_date": "2019-12-01", "type": "conference-paper", "language": "en", "authors": [ "Javier Panadero", "Angel Alejandro Juan", "Alfons Freixes", "Carles Serrat", "Manel Grifoll", "Mohammad Dehghanimohamamdabadi" ], "first_author": "Javier Panadero", "venue": null, "publisher": null, "abstract": "In the unmanned aerial vehicle (UAV) surveillance-routing problem, a limited fleet of UAVs with driving-range limitations have to visit a series of target zones in order to complete a monitoring operation. This operation typically involves taking images and / or registering some key performance indicators. Whenever this surveillance action is repetitive, a natural goal to achieve is to complete each cycle of visits as fast as possible, so that the number of times each target zone is visited during a time interval is maximized. Since many factors might influence travel times, they are modeled as random variables. Reliability issues are also considered, since random travel times might prevent a route from being successfully completed before the UAV runs out of battery. In order to solve this stochastic optimization problem, a simheuristic algorithm is proposed. Computational experiments contribute to illustrate these concepts and to test the quality of our approach.", "keywords": [ "Reliability (semiconductor)", "Computer science", "Routing (electronic design automation)", "Range (aeronautics)", "Interval (graph theory)", "Key (lock)", "Stochastic process", "Real-time computing", "Operations research", "Engineering", "Computer security", "Computer network" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/2117/178421" }, { "id": "https://openalex.org/W4390615190", "doi": "10.35784/acs-2023-36", "title": "IDENTIFYING THE POTENTIAL OF UNMANNED AERIAL VEHICLE ROUTING FOR BLOOD DISTRIBUTION IN EMERGENCY REQUESTS", "year": 2023, "publication_date": "2023-12-31", "type": "article", "language": "en", "authors": [ "Janani DEWMINI", "W. Madushan Fernando", "Izabela Ewa Nielsen", "Grzegorz Bocewicz", "Amila Thibbotuwawa", "Zbigniew Antoni Banaszak" ], "first_author": "Janani DEWMINI", "venue": "Applied Computer Science", "publisher": "Polish Association for Knowledge Promotion", "abstract": "This study is focusing on identifying the potential of Unmanned Aerial Vehicle (UAV) routing for blood distribution in emergency requests in Sri Lanka compared to existing transportation modes. Capacitated Unmanned Aerial Vehicle Routing Problem was used as the methodology to find the optimal distribution plan between blood banks directing emergency requests. The developed UAV routing model was tested for different instances to compare the results. Finally, the proposed distribution process via UAVs was compared with the current distribution process for the objective function set up in the model and other Key Performance Indicators (KPIs) including energy consumption savings and operational cost savings. The average percentage of distribution time re-duction, energy consumption cost reduction, and operational cost per day reduction utilizing UAVs were determined to be 58.57%, 96.35%, and 61.20%, respectively, for the instances tested using the model highlighting the potential of UAVs. Therefore, the deficiencies in Sri Lanka's present blood delivery system can be addressed using UAVs' potential for time, cost, and energy savings. The ability to save time through the deployment of UAVs to the fleet during emergency situations plays a crucial role in preventing the loss of human lives.", "keywords": [ "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Distribution (mathematics)", "Computer network", "Aeronautics", "Real-time computing", "Operations research" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ph.pollub.pl/index.php/acs/article/download/5475/4323" }, { "id": "https://openalex.org/W2952429321", "doi": "10.1063/1.5092740", "title": "Energy-efficient data routing in cooperative UAV swarms for medical assistance after a disaster", "year": 2019, "publication_date": "2019-06-01", "type": "article", "language": "en", "authors": [ "Yuanhao Yang", "Xiaoyu Qiu", "Shenghui Li", "Junbo Wang", "Wuhui Chen", "Patrick C. K. Hung", "Zibin Zheng" ], "first_author": "Yuanhao Yang", "venue": "Chaos An Interdisciplinary Journal of Nonlinear Science", "publisher": "American Institute of Physics", "abstract": "Medical assistance is crucial to disaster management. In particular, the situation of survivors as well as the environmental information after disasters should be collected and sent back to cloud/data centers immediately for further interpretation and analysis. Recently, unmanned aerial vehicle (UAV)-aided disaster management has been considered a promising approach to enhance the efficiency of searching and rescuing survivors after a disaster, in which a group of UAVs collaborates to accomplish the search and rescue task. However, the battery capacity of UAVs is a critical shortcoming that limits their usage. Worse still, the unstable network connectivity of disaster sites could lead to high latency of data transmission from UAV to remote data centers, which could make significant challenges on real-time data collecting and processing. To solve the above problems, in this paper, we investigate an energy-efficient multihop data routing algorithm with the guarantee of quality-of-service for UAV-aided medical assistance. Specifically, we first study the data routing problem to minimize the energy consumption considering transmission rate, time delay, and life cycle of the UAV swarms. Then, we formulate the issue as a mixed-integer nonlinear programming model. Because of the Non-deterministic Polynomial-hardness of this problem, we propose a polynomial time algorithm based on a genetic algorithm to solve the problem. To achieve high efficiency, we further enhance our algorithm based on DBSCAN and adaptive techniques. Extensive experiments show that our approach can outperform the state-of-the-art methods.", "keywords": [ "Computer science", "Energy consumption", "Integer programming", "Cloud computing", "Routing (electronic design automation)", "Distributed computing", "Data transmission", "Real-time computing", "Computer network", "Algorithm", "Engineering" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1063/1.5092740" }, { "id": "https://openalex.org/W2997570606", "doi": null, "title": "A self-adaptive UAV routing for forest fire risk mitigation: a conceptual model", "year": 2019, "publication_date": "2019-07-22", "type": "article", "language": "en", "authors": [ "Sezgin Kιlιç", "Ömer Özkan" ], "first_author": "Sezgin Kιlιç", "venue": null, "publisher": null, "abstract": "Forests have crucial importance for the sustainability of Earth and humanity and one of the biggest threats to the existence of forests are the fires. This paper proposes a conceptual model for mitigating forest fire risk by use of self-adaptive and autonomous unmanned aerial vehicles (UAVs). Memoryless property of exponential distribution is also reflected and considered in the calculations of forest fire probabilities. Stochastic and dynamic properties of the situation and the mathematical complexity of the routing problem entailed and justified a simulation study. The effectiveness of the proposed dispatching approach for routing UAVs and the validity of the proposed model are tested on a small sized realistic scenario. Experimental results encourage the development of complex models. Integrating the proposed model with advanced information technologies may lead to the development of a digital twin system.", "keywords": [ "Routing (electronic design automation)", "Sustainability", "Conceptual model", "Computer science", "Environmental resource management", "Environmental science", "Ecology" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dblp.uni-trier.de/db/conf/scsc/scsc2019.html#KilicO19" }, { "id": "https://openalex.org/W7128630581", "doi": "10.1109/ictmod66732.2025.11371786", "title": "Optimization and Energy Efficiency of Ground Autonomous Vehicles and Drones Systems: Current Approaches and Future Perspectives", "year": 2025, "publication_date": "2025-10-20", "type": "conference-paper", "language": null, "authors": [ "Martina Luzzi", "Khaoula Kharfati", "Luigi Di Puglia Pugliese", "Giusy Macrina", "Francesca Guerriero" ], "first_author": "Martina Luzzi", "venue": null, "publisher": null, "abstract": "This study presents a comprehensive review of recent research addressing routing, scheduling, and operational challenges in autonomous electric vehicles and unmanned aerial vehicles within logistics and mobility systems. Unlike previous surveys that provided broad overviews of vehicle routing problem variants and electric vehicle sharing systems, our review focuses specifically on the integration of autonomy and electrification, with particular attention to collaborative ground and air delivery and shared autonomous mobility. The surveyed works highlight three main directions: the incorporation of realistic energy-related constraints such as nonlinear battery consumption and partial recharging; the diversification of applications ranging from last-mile delivery and warehouse stocktaking to ride-sharing and large-scale shared mobility; and the methodological variety spanning exact optimization, heuristics, metaheuristics, and hybrid approaches. These advances mark a shift toward models that better balance realism and efficiency in autonomous logistics. As a result, the study outlines several promising directions for future research in this area.", "keywords": [ "Drone", "Diversification (marketing strategy)", "Efficient energy use", "Variety (cybernetics)", "Autonomy", "Electric vehicle", "Energy consumption" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ictmod66732.2025.11371786" }, { "id": "https://openalex.org/W2736594259", "doi": "10.1007/978-3-319-63309-1_44", "title": "Research on Multi-UAV Loading Multi-type Sensors Cooperative Reconnaissance Task Planning Based on Genetic Algorithm", "year": 2017, "publication_date": "2017-01-01", "type": "conference-paper", "language": "en", "authors": [ "Ji-Ting Li", "Sheng Zhang", "Zhan Zheng", "Lining Xing", "Renjie He" ], "first_author": "Ji-Ting Li", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Traverse", "Genetic algorithm", "Battlefield", "Real-time computing", "Task (project management)", "Scheduling (production processes)", "Motion planning", "Travelling salesman problem", "Route planning", "Range (aeronautics)", "Vehicle routing problem" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-319-63309-1_44" }, { "id": "https://openalex.org/W4392158214", "doi": "10.1109/globecom54140.2023.10437287", "title": "Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach", "year": 2023, "publication_date": "2023-12-04", "type": "conference-paper", "language": "en", "authors": [ "Sijie He", "Ziye Jia", "Chao Dong", "Wei Wang", "Yilu Cao", "Yang Yang", "Qihui Wu" ], "first_author": "Sijie He", "venue": null, "publisher": null, "abstract": "The unmanned aerial vehicle (UAV) network is popular these years due to its various applications. In the UAV network, routing is significantly affected by the distributed network topology, leading to the issue that UAVs are vulnerable to deliberate damage. Hence, this paper focuses on the routing plan and recovery for UAV networks with attacks. In detail, a deliberate attack model based on the importance of nodes is designed to represent enemy attacks. Then, a node importance ranking mechanism is presented, considering the degree of nodes and link importance. However, it is intractable to handle the routing problem by traditional methods for UAV networks, since link connections change with the UAV availability. Hence, an intelligent algorithm based on reinforcement learning is proposed to recover the routing path when UAVs are attacked. Simulations are conducted and numerical results verify the proposed mechanism performs better than other referred methods.", "keywords": [ "Reinforcement learning", "Computer science", "Routing (electronic design automation)", "Computer network", "Computer security", "Artificial intelligence" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/globecom54140.2023.10437287" }, { "id": "https://openalex.org/W2013193741", "doi": "10.1007/s11067-006-9000-9", "title": "An Automated Network Generation Procedure for Routing of Unmanned Aerial Vehicles (UAVs) in a GIS Environment", "year": 2006, "publication_date": "2006-12-19", "type": "article", "language": "en", "authors": [ "Irene Amil Casas", "Amit Malik", "Eric M. Delmelle", "Mark H. Karwan", "Rajan Batta" ], "first_author": "Irene Amil Casas", "venue": "Networks and Spatial Economics", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Terrain", "Routing (electronic design automation)", "Computer science", "Triangulated irregular network", "Network planning and design", "Real-time computing", "Motion planning", "Process (computing)", "Node (physics)", "Simulation", "Computer network", "Robot" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s11067-006-9000-9" }, { "id": "https://openalex.org/W4415281015", "doi": "10.3390/info16100897", "title": "Strategic Launch Pad Positioning: Optimizing Drone Path Planning Through Genetic Algorithms", "year": 2025, "publication_date": "2025-10-14", "type": "article", "language": "en", "authors": [ "Gregory Gasteratos", "Ioannis Karydis" ], "first_author": "Gregory Gasteratos", "venue": "Information", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Multi-drone operations face significant efficiency challenges when launch pad locations are predetermined without optimization, leading to suboptimal route configurations and increased travel distances. This research addresses launch pad positioning as a continuous planar location-routing problem (PLRP), developing a genetic algorithm framework integrated with multiple Traveling Salesman Problem (mTSP) solvers to optimize launch pad coordinates within operational areas. The methodology was evaluated through extensive experimentation involving over 17 million test executions across varying problem complexities and compared against brute-force optimization, Particle Swarm Optimization (PSO), and simulated annealing (SA) approaches. The results demonstrate that the genetic algorithm achieves 97–100% solution accuracy relative to exhaustive search methods while reducing computational requirements by four orders of magnitude, requiring an average of 527 iterations compared to 30,000 for PSO and 1000 for SA. Smart initialization strategies and adaptive termination criteria provide additional performance enhancements, reducing computational effort by 94% while maintaining 98.8% solution quality. Statistical validation confirms systematic improvements across all tested scenarios. This research establishes a validated methodological framework for continuous launch pad optimization in UAV operations, providing practical insights for real-world applications where both solution quality and computational efficiency are critical operational factors while acknowledging the simplified energy model limitations that warrant future research into more complex operational dynamics.", "keywords": [ "Genetic algorithm", "Initialization", "Particle swarm optimization", "Simulated annealing", "Drone", "Travelling salesman problem", "Motion planning", "Path (computing)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2078-2489/16/10/897/pdf?version=1760494434" }, { "id": "https://openalex.org/W2921867328", "doi": "10.20944/preprints201902.0110.v1", "title": "Cooperated Routing Problem for Ground Vehicle and Unmanned Aerial Vehicle: The Application on Intelligence, Surveillance and Reconnaissance Missions", "year": 2019, "publication_date": "2019-02-13", "type": "preprint", "language": "en", "authors": [ "Yao Liu", "Zhihao Luo", "Zhong Liu", "Jianmai Shi", "Guangquan Cheng" ], "first_author": "Yao Liu", "venue": "Preprints.org", "publisher": null, "abstract": "In this paper, we present a novel Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP). The 2E-GUCRP arises in the field of Unmanned Aerial Vehicle (UAV) Routing Problem such as those encountered in the context of city logistics. In a typical cooperated system, the UAV is launched from the Ground Vehicle (GV) and automatically flies to the designated target. Meanwhile, acting as a mobile base station, the GV can charge or change the UAV’s battery on the designated landing points to enable the UAV to continue its mission. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A Mixed Integer Programming (MIP) model, which could be solved by commercial software, is constructed to describe this problem. In order to quickly solve the medium-scale problems, two existing heuristics to solve 2E-VRP are improved. The computational experiments are set up to compare our model with the 2E-VRP. The results indicate that the 2E-GUCRP obtains a better efficiency. Further discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.", "keywords": [ "Heuristics", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Context (archaeology)", "Flight plan", "Integer programming", "Real-time computing", "Embedded system", "Geography", "Algorithm" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/manuscript/201902.0110/v1/download" }, { "id": "https://openalex.org/W7128516104", "doi": "10.1109/tai.2026.3662657", "title": "Collaborative Truck-Drone Routing With Multivisit via Surrogate-Assisted Bi-Level Optimization", "year": 2026, "publication_date": "2026-02-10", "type": "article", "language": null, "authors": [ "Ruonan Zhai", "Xi Zhang", "Yi Mei", "Wenbo Du", "Tong Guo", "Tao Song" ], "first_author": "Ruonan Zhai", "venue": "IEEE Transactions on Artificial Intelligence", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The development of drone techniques has extensive applications to fields like last-mile parcel delivery. For economic and efficiency advantages, collaborative truck-drone systems have attracted great attention and formulated as truck-drone routing problems. In this paper, a multi-visit traveling salesman problem with drones is investigated. We decompose it into a two-layer structure and formulate it as a new bi-level model (Bi-MTSPD). The major advantage of the bi-level model is its ability to represent the complex interactions between truck and drone routes by dispatching the drones under the premise of truck routes. To solvethe bi-level model, we propose a novel surrogate-assisted bi-level optimization method. The upper-level subproblem can be treated as a traveling salesman problem with customer assignment, and high-quality truck routes are identified by the K-nearest neighbor-based surrogate model with newly proposed features in order to allocate more computational resources to promising upper-level solutions. For the lower-level drone location routing problem (DLR), a customized memetic algorithm is developed to optimize the drone routes for the corresponding truck route. Comprehensive experimental results show that the proposed algorithm significantly outperforms the existing state-of-the-art algorithms across most benchmark instances. Further analysis verifies the congruence of the bi-level optimization for MTSP-D and the effectiveness of upper-level and lower-level solvers.", "keywords": [ "Drone", "Travelling salesman problem", "Vehicle routing problem", "Routing (electronic design automation)", "Benchmark (surveying)", "Truck", "2-opt" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tai.2026.3662657" }, { "id": "https://openalex.org/W4406548147", "doi": "10.48550/arxiv.2501.09249", "title": "A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring", "year": 2025, "publication_date": "2025-01-16", "type": "preprint", "language": "en", "authors": [ "Yumeng Bai", "Yiheng Feng" ], "first_author": "Yumeng Bai", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned Aerial Vehicles (UAVs) have great potential in urban traffic monitoring due to their rapid speed, cost-effectiveness, and extensive field-of-view, while being unconstrained by traffic congestion. However, their limited flight duration presents critical challenges in sustainable recharging strategies and efficient route planning in long-term monitoring tasks. Additionally, existing approaches for long-term monitoring often neglect the evolving nature of urban traffic networks. In this study, we introduce a novel dynamic UAV routing framework for long-term, network-wide urban traffic monitoring, leveraging existing ground vehicles as mobile charging stations without disrupting their operations. To address the complexity of long-term monitoring scenarios involving multiple flights, we decompose the problem into manageable single-flight tasks, in which each flight is modeled as a Team Arc Orienteering Problem with Decreasing Profits with the objective to collectively maximize the spatiotemporal network coverage. Between flights, we adaptively update the edge weights to incorporate real-time traffic changes and revisit intervals. We validate our framework through extensive microscopic simulations in a modified Sioux Falls network under various scenarios. Comparative results demonstrate that our model outperforms three baseline approaches, especially when historical information is incomplete or absent. Moreover, we show that our monitoring framework can capture network-wide traffic trends and construct accurate Macroscopic Fundamental Diagrams (MFDs). These findings demonstrate the effectiveness of the proposed dynamic UAV routing framework, underscoring its suitability for efficient and reliable long-term traffic monitoring. Our approach's adaptability and high accuracy in capturing the MFD highlight its potential in network-wide traffic control and management applications.", "keywords": [ "Routing (electronic design automation)", "Computer science", "Adaptive routing", "Transport engineering", "Vehicle routing problem", "Real-time computing", "Aeronautics", "Computer network", "Static routing", "Engineering", "Routing protocol" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2501.09249" }, { "id": "https://openalex.org/W4317792294", "doi": "10.1109/wsc57314.2022.10015320", "title": "An Adaptive Large Neighborhood Search Algorithm for Wind Farm Inspection Using a Truck with a Drone", "year": 2022, "publication_date": "2022-12-11", "type": "conference-paper", "language": "en", "authors": [ "Wenyu Tao", "Xinjia Jiang", "Dongqiang Zhao" ], "first_author": "Wenyu Tao", "venue": null, "publisher": null, "abstract": "With the increasing demand for energy, wind power as a new energy source has been widely used and developed on a large scale. To extend the life of wind turbines, it is necessary but difficult to carry out regular inspections in wind farms located in remote areas. This paper studied the clustering and routing problem of truck-drone joint inspection of wind farms. An Adaptive Large Neighborhood Search (ALNS) algorithm is designed based on the characteristics of this problem. In addition, wind farm instances with different sizes and distributions are generated in this paper to simulate realistic scenes and evaluate ALNS. Finally, real wind farm instances are tested to demonstrate the inspection time in detail. Computational experiments show ALNS can improve significantly inspection time compared with another method.", "keywords": [ "Drone", "Wind power", "Truck", "Cluster analysis", "Computer science", "Scale (ratio)", "Wind speed", "Routing (electronic design automation)", "Real-time computing", "Algorithm", "Simulation", "Automotive engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/wsc57314.2022.10015320" }, { "id": "https://openalex.org/W4285326472", "doi": "10.1109/hpcc-dss-smartcity-dependsys53884.2021.00202", "title": "HIVE: Route Optimization Problem of Automatic Delivery System with Mobile Depot and Multiple Drones", "year": 2021, "publication_date": "2021-12-01", "type": "conference-paper", "language": "en", "authors": [ "Yao Liu", "Xuening Chang", "Yuzhen Zhou", "Shuo Dang", "Jianmai Shi", "Zhong Liu" ], "first_author": "Yao Liu", "venue": null, "publisher": null, "abstract": "An automatic delivery system with mobile depot and multiple drones, HIVE, is proposed. Through this system and a series of embedded devices such as drone automatic launch and recovery device etc., two delivery modes, manual delivery and unmanned delivery, are executed in parallel. To solve the delivery route of this system, Two-Echelon Routing Problem with Mobile Depot and Multiple Drones (2E-RP-MD&mD) is investigated. A heuristic algorithm based on the regression is designed according to the route characteristics of the HIVE delivery system. Parameters of mobile depot and drones are standardized referred to the performance of delivery vehicles. Experiments on random instances as well as actual case based on Changsha are carried out to test the performance of the proposed algorithm and prove the delivery efficiency of HIVE automatic delivery system.", "keywords": [ "Drone", "Depot", "Computer science", "Heuristic", "Routing (electronic design automation)", "Delivery system", "Real-time computing", "Simulation", "Embedded system", "Engineering", "Artificial intelligence" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/hpcc-dss-smartcity-dependsys53884.2021.00202" }, { "id": "https://openalex.org/W7162757153", "doi": "10.1016/j.tbs.2026.101320", "title": "Emerging multimodal truck-drone routing with customer priority for equitable post-disaster relief", "year": 2026, "publication_date": "2026-05-29", "type": "article", "language": "en", "authors": [ "Guizhen Chen", "Chenghao Liu", "Xiaolong Ma", "Y B Li", "H T Wang" ], "first_author": "Guizhen Chen", "venue": "Travel Behaviour and Society", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Scheduling (production processes)", "Vehicle routing problem", "Routing (electronic design automation)", "Prioritization", "Service (business)", "Linear programming", "Flow network", "Selection (genetic algorithm)", "City logistics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tbs.2026.101320" }, { "id": "https://openalex.org/W4206631234", "doi": "10.1016/j.procs.2021.12.140", "title": "Multi-UAV routing for maximum surveillance data collection with idleness and latency constraints", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Baiq Zuyyina Hilyatur Rozaliya", "I-Lin Wang", "Ahmad Muklason" ], "first_author": "Baiq Zuyyina Hilyatur Rozaliya", "venue": "Procedia Computer Science", "publisher": "Elsevier BV", "abstract": "This study discusses a multi-UAV surveillance routing problem that routes a UAV fleet from a base station to periodically capture data from sensing locations in a store-and-forward fashion during the planning horizon. Cooperated UAVs can wirelessly transfer data within communication range to gain the maximum collected data received at the base station while satisfying the idleness and latency constraints. An integer programming (IP) model is proposed on a time-space network to track the data movement and compared to an uncooperative data transport model. The results indicate that the proposed model transfers more data with less latency and idleness.", "keywords": [ "Computer science", "Latency (audio)", "Base station", "Real-time computing", "Integer programming", "Routing (electronic design automation)", "Time horizon", "Computer network", "Range (aeronautics)", "Telecommunications", "Mathematical optimization", "Algorithm" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S1877050921023644/pdf" }, { "id": "https://openalex.org/W4416985828", "doi": "10.1038/s41598-025-30410-w", "title": "Reliability-oriented framework for UAV-based inspection missions in modern power and energy systems", "year": 2025, "publication_date": "2025-12-04", "type": "article", "language": "en", "authors": [ "Luttfi Ahmed Al-Haddad", "Wissam Khalid", "Sarmad Tariq", "Muhannad M. Mrah", "Aymen Flah", "Ahmad F. Tazay", "Alaa Abdulhady Jaber" ], "first_author": "Luttfi Ahmed Al-Haddad", "venue": "Scientific Reports", "publisher": "Nature Portfolio", "abstract": "Ensuring mission reliability is vital for the autonomous deployment of unmanned aerial vehicles (UAVs) in modern power and energy systems, particularly under spatial and operational constraints. This study presents a data-driven classification method that assesses the reliability of UAV-based inspection missions by identifying whether individual mission locations are suitable, at risk, or infeasible based on spatial and operational parameters. Leveraging the Cumulative UAV Routing Problem (CUAVRP) benchmark, four representative mission scenarios were analyzed, each characterized by unique UAV fleet sizes, sensor ranges, and endurance limits. Synthetic stress nodes were introduced to emulate edge-case conditions encountered in infrastructure inspection tasks. Each node was classified based on three categorical targets: Mission Feasibility, Coverage Reliability, and Deployment Suitability. A gradient boosting classification model was trained on spatial and operational features to determine node status. Evaluation across all scenarios yielded consistently high performance, with the cuavrp_d9_k6_r800 scenario achieving 97.05% accuracy, 96.33% precision, 97.72% recall, and 97.02% F1-score. Furthermore, incorporating physical-layer degradation factors such as signal attenuation, multipath fading, and interference is expected to enhance the realism of future reliability assessments and improve classification robustness. The proposed classification framework supports intelligent mission planning, enhances operational resilience, and facilitates automated UAV deployment strategies in critical inspection environments within the power and energy sector.", "keywords": [ "Software deployment", "Reliability (semiconductor)", "Node (physics)", "Search and rescue", "Wireless sensor network", "Categorical variable", "Energy (signal processing)", "Energy consumption" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.nature.com/articles/s41598-025-30410-w_reference.pdf" }, { "id": "https://openalex.org/W3137998270", "doi": null, "title": "Heterogeneous Facility Location under Coordination and Collaboration: The Case of Ambulance-Bystander-Drone Coordination", "year": 2020, "publication_date": "2020-01-14", "type": "preprint", "language": "en", "authors": [ "Jungeun Shin", "Lavanya Marla", "Justin J. Boutilier" ], "first_author": "Jungeun Shin", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": "Drone systems are emerging technology for emergency care of out-of-hospital arrest due to their mobility for quick response to emergency requests. Emergency drone systems can facilitate early medical intervention by delivering automated external defibrillators (AED) before the arrival of traditional ambulances. Despite the AED being delivered to the scene quickly, administration of service cannot be done in the absence of a bystander. Existing works on drone-delivered AED location ignore the stochasticity in bystander response. In this study, we consider location-allocation problems for drones and ambulances to ensure that critical emergencies can be served within the stringent service thresholds with a pre-specified probability. Service can be achieved by either fast ambulances or drone-delivered AED and a bystander, complemented by a slower-arriving ambulance. We develop an optimization framework to minimize the additional required drones to reach the service level, under a given ambulance fleet size (or allocation); and explicitly modeling heterogeneous bystander response times. The joint probabilistic constraint is described and its linear equivalent constraint with approximation is developed to solve the problem as mixed integer linear program. Our results indicate the importance of modeling bystander response time to make drone-delivered healthcare systems successful, and also, the necessity of raising awareness to increase bystander participation.", "keywords": [ "Drone", "Bystander effect", "Service (business)", "Constraint (computer-aided design)", "Probabilistic logic", "Ambulance service", "Emergency medical services", "Medical emergency", "Computer science", "Operations research", "Computer security", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3519243" }, { "id": "https://openalex.org/W4409712520", "doi": "10.1177/22779752251329395", "title": "A Systematic Review and Morphological Analysis of the Literature on Last Mile Delivery Using Drones: Quo Vadis?", "year": 2025, "publication_date": "2025-04-22", "type": "review", "language": "en", "authors": [ "G Rohit", "Ramesh Krishnan", "P. N. Ram Kumar" ], "first_author": "G Rohit", "venue": "IIM Kozhikode Society & Management Review", "publisher": "SAGE Publishing", "abstract": "With the evolution of technology, drones hold immense potential to address the last mile delivery challenges in e-commerce. However, a holistic and systematic literature review of all the studies and an understanding of the interrelationships between the different dimensions of drone-based last mile delivery, such as customer expectations, stakeholders and desired outcomes, is lacking in the literature. To address this gap, we conducted a systematic literature review and analysed the literature on drone-based last mile delivery using a morphological analysis approach to develop a conceptual framework with seven dimensions and 37 variants. The dimensions identified are customer expectations, distribution mode, inventory planning, returns handling, geographical scope, stakeholders and desired outcomes. The Variance Intersection Matrix revealed 176 research gaps that future researchers can explore. This study also helps managers and policymakers visualize the different dimensions of drone-based last mile delivery and their interrelationships, which will help accelerate the adoption of drones in delivery fleets.", "keywords": [ "Mile", "Drone", "Status quo", "Business", "Political science", "Computer science", "Geography", "Law" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/22779752251329395" }, { "id": "https://openalex.org/W3159677029", "doi": "10.1007/978-3-030-75100-5_51", "title": "UAVs Route Planning in Sea Emergencies", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Nicholas Formica", "Leonardo Mostarda", "Alfredo Navarra" ], "first_author": "Nicholas Formica", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Dispose pattern", "Exploit", "Computer science", "Routing (electronic design automation)", "Route planning", "Search and rescue", "Aeronautics", "Operations research", "Engineering", "Computer security", "Transport engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-75100-5_51" }, { "id": "https://openalex.org/W1542734564", "doi": "10.1007/978-3-540-68123-6_27", "title": "A Multi-objective Optimal Approach for UAV Routing in Reconnaissance Mission with Stochastic Observation Time", "year": 2008, "publication_date": "2008-05-09", "type": "conference-paper", "language": "en", "authors": [ "Xingguang Peng", "Xiaoguang Gao" ], "first_author": "Xingguang Peng", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Routing (electronic design automation)", "Real-time computing", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-540-68123-6_27" }, { "id": "https://openalex.org/W7133334238", "doi": "10.1016/j.team.2026.02.001", "title": "Air-ground collaborative routing problem for parcel deliveries under soft time windows", "year": 2026, "publication_date": "2026-03-03", "type": "article", "language": "en", "authors": [ "Rongfei Du", "W. D. Sun", "Fangni Zhang", "Jinping Guan" ], "first_author": "Rongfei Du", "venue": "Transport Economics and Management", "publisher": "Elsevier BV", "abstract": "Rapid growth in e-commerce is putting unprecedented pressure on urban logistics to reduce costs, emissions, and delivery times. Leveraging complementary strengths of ground vehicles and drones, this study investigates an Electric Vehicle and Drone Routing Problem with soft Time Windows (EVRPD-TW) for parcel deliveries. Specifically, the system consists of multiple Electric Unmanned Ground Vehicles (E-UGVs), each capable of deploying a drone at one node to serve a customer and retrieving it at another. Therefore, customers may be served by either E-UGVs or drones, depending on energy-endurance constraints. The objective is to minimize the total cost, including the travel cost, vehicle activation fees, and penalties for early or late deliveries beyond time windows. To solve the proposed problem, we design a two-level Hybrid Genetic Algorithm with Dynamic Iteration (HGA-DI). The HGA-DI adopts a permutation chromosome that encodes the sequence of customer visits for each E-UGV, and explores the space of ground routes by classical genetic operators. Moreover, the HGA-DI is integrated with a dynamic iteration method that optimizes drone launches for every candidate route, guaranteeing solution feasibility. We evaluate the proposed problem in a real-world scenario in Shenzhen, China. The HGA-DI can achieve the optimal solutions on small instances over thirty times faster than Gurobi. Compared to vehicle-only delivery, the E-UGVs and drones collaborative system can better meet the time windows and reduce the total cost by 1.2%. Sensitivity analyses reveal that the total cost is most sensitive to the relative per-kilometer travel cost of E-UGVs and drones, followed by time-window penalty rates and drone endurance. These findings demonstrate the economic and environmental benefits of applying air-ground collaborations and offer practical insights for implementing the proposed system in urban logistics.", "keywords": [ "Drone", "Routing (electronic design automation)", "Genetic algorithm", "Vehicle routing problem", "Node (physics)", "Dynamic programming", "Chromosome" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.team.2026.02.001" }, { "id": "https://openalex.org/W4283695239", "doi": "10.1093/imaman/dpac006", "title": "Last mile logistics: Research trends and needs", "year": 2022, "publication_date": "2022-06-29", "type": "article", "language": "en", "authors": [ "Emrah Demir", "Aris A Syntetos", "Tom Van Woensel" ], "first_author": "Emrah Demir", "venue": "IMA Journal of Management Mathematics", "publisher": "Oxford University Press", "abstract": "Abstract Aspiring green agendas in conjunction with tremendous economic pressures are resulting in an increased attention to the environment and technological innovations for improving existing logistics systems. Last mile logistics, in particular, are becoming much more than a consumer convenience necessity and a transportation optimization exercise. Rather, this area presents a true opportunity to foster both financial and environmental sustainability. This paper investigates recent technological advancements and pending needs related to business and social innovations, emphasizing green logistics and city logistics concepts. We discuss various pertinent aspects, including drones, delivery robots, truck platooning, collection and pickup points, collaborative logistics, integrated transportation, decarbonization and advanced transport analytics. From a mathematical perspective, we focus on the basic features of the vehicle routing problem and some of its variants. We provide recommendations around strategies that may facilitate the adoption of new effective technologies and innovations.", "keywords": [ "Last mile (transportation)", "Drone", "Humanitarian Logistics", "Truck", "Sustainability", "Business", "Analytics", "Supply chain", "City logistics", "Mile", "Computer science", "Transport engineering" ], "cited_by_count": 78, "is_retracted": false, "is_paratext": false, "open_access_url": "https://academic.oup.com/imaman/article-pdf/33/4/549/45047530/dpac006.pdf" }, { "id": "https://openalex.org/W4415820524", "doi": "10.3390/drones9110759", "title": "Operational Performance of a 3D Urban Aerial Network and Agent-Distributed Architecture for Freight Delivery by Drones", "year": 2025, "publication_date": "2025-11-01", "type": "article", "language": "en", "authors": [ "Maria Nadia Postorino", "Giuseppe M. L. Sarnè" ], "first_author": "Maria Nadia Postorino", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The growing demand for fast and sustainable urban deliveries has accelerated exploration of the use of Unmanned Aerial Vehicles as viable logistics solutions for the last mile. This study investigates the integration of a distributed multi-agent system with a structured three-dimensional Urban Aerial Network (3D-UAN) for drone delivery operations. The proposed architecture models each drone as an autonomous agent operating within predefined air corridors and communication protocols. Unlike traditional approaches, which rely on simplified 2D models or centralized control systems, this research exploits a multi-layered 3D network structure combined with decentralized decision-making for improving scalability, safety, and responsiveness in complex environments. Through agent-based simulations, this study evaluates the operational performance of the proposed system under varying fleet size conditions, focusing on travel times and system scalability. Preliminary results demonstrate that the potential of this approach in supporting efficient, adaptive, resilient logistics within Urban Air Mobility frameworks depends on both the size of the fleet operating in the 3D-UAN and constraints linked to the current regulations and technological properties, such as the maximum allowed operational height. These findings contribute to ongoing efforts to define robust operational architectures and simulation methodologies for next-generation urban freight transport systems.", "keywords": [ "Drone", "Exploit", "Architecture", "Systems architecture", "Control (management)", "Network architecture", "Operational efficiency" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones9110759" }, { "id": "https://openalex.org/W2810266559", "doi": "10.1007/978-3-319-94346-6_6", "title": "Investigation of Unmanned Aerial Vehicle Routes to Fulfill Power Line Management Tasks", "year": 2018, "publication_date": "2018-06-23", "type": "conference-paper", "language": "en", "authors": [ "Gunta Strupka", "Ivars Raņķis" ], "first_author": "Gunta Strupka", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Computer science", "Line (geometry)", "Artificial intelligence", "Aeronautics", "Engineering", "Mathematics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-319-94346-6_6" }, { "id": "https://openalex.org/W4306398866", "doi": "10.7717/peerj-cs.1119", "title": "Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning", "year": 2022, "publication_date": "2022-10-17", "type": "article", "language": "en", "authors": [ "Minjae Jung", "Hyondong Oh" ], "first_author": "Minjae Jung", "venue": "PeerJ Computer Science", "publisher": "PeerJ, Inc.", "abstract": "Large-scale and complex mission environments require unmanned aerial vehicles (UAVs) to deal with various types of missions while considering their operational and dynamic constraints. This article proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV. We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP). Then, we solve this by using an attention-based deep reinforcement learning approach. Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP. For the input to the attention-based neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors. In addition, a masking strategy is introduced to be able to consider the resource constraint ( e.g ., flight time) of the UAV. Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance.", "keywords": [ "Reinforcement learning", "Computer science", "Artificial intelligence", "Computation", "Masking (illustration)", "Feature (linguistics)", "Artificial neural network", "Constraint (computer-aided design)", "Resource (disambiguation)", "Route planning", "Representation (politics)", "Real-time computing" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7717/peerj-cs.1119" }, { "id": "https://openalex.org/W2798363178", "doi": "10.1109/tase.2019.2931894", "title": "Cooperative Routing for an Air–Ground Vehicle Team—Exact Algorithm, Transformation Method, and Heuristics", "year": 2019, "publication_date": "2019-08-14", "type": "article", "language": "en", "authors": [ "Satyanarayana G. Manyam", "Kaarthik Sundar", "David W. Casbeer" ], "first_author": "Satyanarayana G. Manyam", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper considers a cooperative vehicle routing problem for an intelligence, surveillance, and reconnaissance mission in the presence of communication constraints between the vehicles. The proposed framework uses a ground vehicle and an unmanned aerial vehicle (UAV) that travel cooperatively and visit a set of targets while satisfying the communication constraints. The problem is formulated as a mixed-integer linear program, and a branch-and-cut algorithm is developed to solve the problem to optimality. Furthermore, a transformation method and a heuristic are also developed for the problem. The effectiveness of all the algorithms is corroborated through extensive computational experiments on several randomly generated instances. This paper is motivated by an intelligence, surveillance, and reconnaissance mission involving a single unmanned aerial vehicle (UAV) and a ground vehicle, where the vehicles must coordinate their activity in the presence of communication constraints. The combination of a small UAV and a ground vehicle is an ideal platform for such missions, since small UAVs can fly at low altitudes and can avoid obstacles or threats that would be problematic for the ground vehicle alone. This paper addresses the coordinated routing problem involving these two vehicles and presents an algorithm to obtain an optimal solution for this problem, fast heuristics to obtain good feasible solutions, and also a transformation method to transform any instance of this cooperative vehicle routing problem to an instance of the one-in-a-set traveling salesman problem.", "keywords": [ "Heuristics", "Routing (electronic design automation)", "Transformation (genetics)", "Vehicle routing problem", "Computer science", "Algorithm", "Engineering", "Mathematical optimization", "Mathematics", "Embedded system" ], "cited_by_count": 64, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1804.09546" }, { "id": "https://openalex.org/W3203533207", "doi": "10.1007/978-3-030-88081-1_17", "title": "Improving Pheromone Communication for UAV Swarm Mobility Management", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Daniel H. Stolfi", "Matthias R. Brust", "Grégoire Danoy", "Pascal Bouvry" ], "first_author": "Daniel H. Stolfi", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Swarm behaviour", "Reliability (semiconductor)", "Ant colony optimization algorithms", "Network packet", "Chaotic", "Genetic algorithm", "Swarm intelligence", "Ant colony", "Real-time computing", "Distributed computing", "Simulation" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://orbilu.uni.lu/handle/10993/53507" }, { "id": "https://openalex.org/W2287676973", "doi": "10.1007/s00500-015-1970-4", "title": "Efficient route planning for an unmanned air vehicle deployed on a moving carrier", "year": 2015, "publication_date": "2015-12-07", "type": "article", "language": "en", "authors": [ "Halil Savuran", "Murat Karakaya" ], "first_author": "Halil Savuran", "venue": "Soft Computing", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Travelling salesman problem", "Vehicle routing problem", "Heuristics", "Benchmark (surveying)", "Constraint (computer-aided design)", "Genetic algorithm", "Computer science", "Range (aeronautics)", "Routing (electronic design automation)", "Mathematical optimization", "Route planning", "Constraint programming" ], "cited_by_count": 104, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s00500-015-1970-4" }, { "id": "https://openalex.org/W2913494453", "doi": "10.22178/pos.42-1", "title": "Formalized Resolution of the Scientific Task for Improving the Effective Planning Special Purpose Unmanned Aerial Vehicles Routes of Tactical Level", "year": 2019, "publication_date": "2019-01-31", "type": "article", "language": "en", "authors": [ "Ruslan Hryshchuk", "Yuriy Bondarenko", "В.Я. Горбач" ], "first_author": "Ruslan Hryshchuk", "venue": "Path of Science", "publisher": "Altezoro s.r.o. (Slovak Republic) and Publishing Center \"Dialog\" (Ukraine)", "abstract": "In the article, the features of application of special purpose unmanned aerial vehicles (UAVs) of the tactical level have resulted during the operation of the joint forces. The role of routes planning of special purpose UAVs of tactical level in the conditions of a concrete battle situation is determined. The necessity of the mathematical development for planning a flight route of special purpose UAVs is justified. Essence and maintenance of scientific task are set. Its formalized setting is guided.", "keywords": [ "Task (project management)", "Aeronautics", "Computer science", "Battle", "Resolution (logic)", "Set (abstract data type)", "Operations research", "Systems engineering", "Process management", "Engineering", "Artificial intelligence", "Geography" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://pathofscience.org/index.php/ps/article/download/584/599" }, { "id": "https://openalex.org/W2047374581", "doi": "10.1016/j.cor.2006.09.013", "title": "Priority-based assignment and routing of a fleet of unmanned combat aerial vehicles", "year": 2006, "publication_date": "2006-11-02", "type": "article", "language": "en", "authors": [ "Vijay K. Shetty", "Moises Sudit", "Rakesh Nagi" ], "first_author": "Vijay K. Shetty", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Payload (computing)", "Computer science", "Tabu search", "Travelling salesman problem", "Operations research", "Heuristic", "Routing (electronic design automation)", "Service (business)", "Mathematical optimization", "Vehicle routing problem", "Branch and bound", "Range (aeronautics)" ], "cited_by_count": 144, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2006.09.013" }, { "id": "https://openalex.org/W7117236099", "doi": "10.5267/j.ijiec.2025.9.002", "title": "Minimizing customer waiting time in drone delivery systems: An optimization approach considering heterogeneous fleets and package setup time using modified coot algorithms", "year": 2025, "publication_date": "2025-12-25", "type": "article", "language": null, "authors": [ "Murat Şahin" ], "first_author": "Murat Şahin", "venue": "International Journal of Industrial Engineering Computations", "publisher": "Growing Science", "abstract": "This study addresses the drone delivery problem with a unique focus on minimizing total customer waiting times, considering the heterogeneous nature of drones and the setup times required for loading customer demands. Unlike traditional routing problems that prioritize cost and route optimization, this research emphasizes timely deliveries, which are critical in both commercial and humanitarian applications. The study introduces two mathematical models and four versions of the coot optimization algorithm, including three modified variants and one classical version. These algorithms incorporate new movement mechanisms, enhanced leader selection strategies, and adaptations of the regenerating strategy to efficiently solve the drone delivery problem. Computational experiments reveal that one modified coot optimization algorithm significantly outperforms the classical version, offering valuable insights into both coot optimization literature and the drone delivery problem. By emphasizing the importance of timely deliveries, this research provides effective solution strategies applicable to both commercial and humanitarian contexts.", "keywords": [ "Drone", "Optimization problem", "Vehicle routing problem", "Selection (genetic algorithm)", "Routing (electronic design automation)", "Optimization algorithm" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5267/j.ijiec.2025.9.002" }, { "id": "https://openalex.org/W4406840866", "doi": "10.48550/arxiv.2501.14325", "title": "Joint Infrastructure Planning and Order Assignment for On-Demand Food-Delivery Services with Coordinated Drones and Human Couriers", "year": 2025, "publication_date": "2025-01-24", "type": "preprint", "language": "en", "authors": [ "Liu, Yang", "Yitong Shang", "Sen Li" ], "first_author": "Liu, Yang", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "This paper investigates the optimal infrastructure planning and order assignment problem of an on-demand food-delivery platform with a mixed fleet of drones and human couriers. The platform has two delivery modes: (a) ground delivery and (b) drone-assisted delivery (i.e., air delivery). In ground delivery, couriers directly collect and transport orders from restaurants to destinations. For air delivery, the delivery process involves three legs: initially, a human courier picks up the order from the restaurant and transports it to a nearby launchpad, where personnel load the orders onto drones and replace batteries as needed. The loaded drone then transports the order from the launchpad to a kiosk, where another courier retrieves the order from the kiosk for final delivery. The platform must determine the optimal locations for launchpads and kiosks within a transportation network, and devise an order assignment strategy that allocates food-delivery orders between ground and air delivery considering the bundling probabilities of ground deliveries and the waiting times at launchpads and kiosks. We formulate the platform's problem as a mixed-integer nonlinear program and develop a novel neural network-assisted optimization method to obtain high-quality solutions. A case study in Hong Kong validates our model and algorithm, revealing that drone delivery reduces operational costs, minimizes courier fleet size, and increases order bundling opportunities. We also find that the expansion of air delivery services may entail larger delivery times due to the trade-off between the travel time savings induced by the faster air delivery and the associated detours incurred by intermodal transfer and extra waiting times at launchpads and kiosks, which crucially depends on the distance of the orders and the sequence of activating long-distance air delivery routes versus short-distance ones.", "keywords": [ "Drone", "Joint (building)", "Order (exchange)", "Business", "Food delivery", "Operations research", "Computer science", "Engineering", "Marketing", "Finance", "Civil engineering", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2501.14325" }, { "id": "https://openalex.org/W2915706907", "doi": "10.1109/cyberc.2018.00085", "title": "A Cooperative Task Assignment Method of Multi-UAV Based on Self Organizing Map", "year": 2018, "publication_date": "2018-10-01", "type": "conference-paper", "language": "en", "authors": [ "Shurong Zhu", "Yingzhou Zhang", "Yang Gao", "Fan Wu" ], "first_author": "Shurong Zhu", "venue": null, "publisher": null, "abstract": "Cooperative task assignment of multi unmanned aerial vehicles (UAV) is an important assurance for UAV to complete military missions. The current task assignment methods have drawbacks such as long execution time and failure to consider UAV's own capacity constraints. Therefore in this paper, we comprehensively considers the total flight distance of the fleet and the weapon load of each UAV, models the cooperative task assignment problem of multi-UAV as a capacitated vehicle routing problem (CVRP). A method based on a competitive neural network called self-organizing feature map (SOM) is used to solve the task assignment problem. The performance of proposed method is compared to that of ant colony algorithm. Experimental results show the feasibility and efficiency of the method.", "keywords": [ "Task (project management)", "Computer science", "Feature (linguistics)", "Assignment problem", "Ant colony optimization algorithms", "Self-organizing map", "Artificial intelligence", "Routing (electronic design automation)", "Artificial neural network", "Real-time computing", "Mathematical optimization", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cyberc.2018.00085" }, { "id": "https://openalex.org/W3106861869", "doi": "10.1109/lwc.2020.3041707", "title": "Multiuser Oriented Multi-UAV Mission Assignment With Cooperative Information Sharing", "year": 2020, "publication_date": "2020-12-02", "type": "article", "language": "en", "authors": [ "Cuntao Liu", "Yan Guo", "Ning Li", "Bin Zhou", "Wendong Zhao" ], "first_author": "Cuntao Liu", "venue": "IEEE Wireless Communications Letters", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Due to the overlap of different users' information requirements for reconnaissance targets, when unmanned aerial vehicles (UAVs), serving multiple users, perform field reconnaissance missions, it is likely to cause repetitive visit to the same targets. To deal with this problem, a reasonable cooperative mission assignment algorithm is needed. By introducing information sharing mechanism with air communication among UAVs, this letter models the mission assignment problem as a vehicle routing problem with air information sharing (VRPAIS), which can efficiently avoid the repetitive visit of targets. An improved piecewise genetic algorithm with integer splitting is proposed to solve VRPAIS in polynomial time. Simulation results show that in the case of high overlap (about 80%) of information requirements, the proposed method is much more energy efficient compared with the traditional Multiple Traveling Salesman Problem (MTSP) based method without information sharing, with an average energy savings rate of 34.2%.", "keywords": [ "Computer science", "Travelling salesman problem", "Information sharing", "Piecewise", "Genetic algorithm", "Energy (signal processing)", "Drone", "Mathematical optimization", "Real-time computing", "Distributed computing", "Algorithm", "Machine learning" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/lwc.2020.3041707" }, { "id": "https://openalex.org/W2312429991", "doi": "10.2514/6.2013-1036", "title": "Experimental Validation of the Allocation of UAVs under Communication Restrictions", "year": 2013, "publication_date": "2013-01-05", "type": "conference-paper", "language": "en", "authors": [ "Chelsea M. Sabo", "Kelly De Oliveira Cohen" ], "first_author": "Chelsea M. Sabo", "venue": "51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition", "publisher": null, "abstract": "A subset of Vehicle Routing Problems (VRPs) address the problem in which a vehicle is required to service targets by picking them up at an origin and deliver them to their destination. With respect to surveillance functions, this becomes a realistic problem as Unmanned Aerial Vehicles (UAVs) are restricted by operating range, data rate, Anti-Jam margins, and cost. Therefore, UAVs must be allocated to pickup targets and then deliver them to a communication range to be able to transmit information back to a command and control HQ. Because most VRPs adopt a minimum distance objective function, this can lead to suboptimal results when information is time critical. Related research developed a new VRP formulation and cost function (minimum delivery latency) and a heuristic for this problem with near-optimal performance and almost linear scalability to address this deficiency. To highlight the usability of this approach, experimental testing in a laboratory environment was completed. Both static and dynamic cases were validated in flight testing on commercially available platforms. Furthermore, the heuristic solutions to the minimum delivery latency objective function were compared to optimal solutions for the minimum distance method to demonstrate how much time can be saved in more realistic scenarios using the new approach.", "keywords": [ "Scalability", "Computer science", "Latency (audio)", "Heuristic", "Usability", "Vehicle routing problem", "Range (aeronautics)", "Function (biology)", "Pickup", "Real-time computing", "Mathematical optimization", "Simulation" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2013-1036" }, { "id": "https://openalex.org/W4405786774", "doi": "10.1109/iros58592.2024.10801704", "title": "An Attention-aware Deep Reinforcement Learning Framework for UAV-UGV Collaborative Route Planning", "year": 2024, "publication_date": "2024-10-14", "type": "conference-paper", "language": "en", "authors": [ "Md Safwan Mondal", "Subramanian Ramasamy", "James D. Humann", "James M. Dotterweich", "Jean-Paul Reddinger", "Marshal Childers", "Pranav A. Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": null, "publisher": null, "abstract": "Unmanned aerial vehicles (UAVs) possess the capability to survey vast areas, yet their operational range is limited by their battery capacity. Deploying mobile recharging stations via unmanned ground vehicles (UGVs) can significantly enhance the endurance and effectiveness of UAVs. However, optimizing the routes for both UAVs and UGVs, referred to as the UAV-UGV cooperative routing problem, requires a sophisticated planning framework to determine the vehicles’ routes and their recharging points. To address this, in this paper, we utilize a deep reinforcement learning (DRL) based framework equipped with multi-head attention layers. The framework is designed to sequentially select actions to construct routes for the UAV and UGV and to establish their rendezvous points for recharging. We evaluate our framework across various problem instance sizes and distributions, comparing it against recent heuristic-based methods and an existing learning-based method as baselines. Our proposed algorithm surpasses these baselines in terms of solution quality and runtime efficiency in the test scenarios, thus proving its effectiveness. Additionally, we investigate the application of our DRL policy in online mission planning to accommodate dynamic changes within the mission scenario.", "keywords": [ "Reinforcement learning", "Computer science", "Human–computer interaction", "Artificial intelligence", "Unmanned ground vehicle" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iros58592.2024.10801704" }, { "id": "https://openalex.org/W4406569435", "doi": "10.2139/ssrn.5102713", "title": "Mixed Inspection Mission Based Multi-Depot Uav Swarm Routing Problem in Transmission Line: Milp Formulation and Efficient Metaheuristic", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Xiaohui Li", "Yuchen LIU", "Lu Zhang", "Yi Zhao", "Yuan Dong", "Yinli Jin" ], "first_author": "Xiaohui Li", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Metaheuristic", "Swarm behaviour", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Mathematical optimization", "Line (geometry)", "Transmission (telecommunications)", "Operations research", "Engineering", "Computer network", "Artificial intelligence" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5102713" }, { "id": "https://openalex.org/W2747616223", "doi": "10.2197/ipsjjip.25.655", "title": "Routing of Carrier-vehicle Systems with Dedicated Last-stretch Delivery Vehicle and Fixed Carrier Route", "year": 2017, "publication_date": "2017-01-01", "type": "article", "language": "en", "authors": [ "Mohd Shahrizan Othman", "Aleksandar Shurbevski", "Yoshiyuki Karuno", "Hiroshi Nagamochi" ], "first_author": "Mohd Shahrizan Othman", "venue": "Journal of Information Processing", "publisher": null, "abstract": "We examine a routing problem that arises when an unmanned aerial vehicle (UAV), or drone, is used in the last-stretch of parcel delivery to end customers. In the scenario that we study, a delivery truck is dispatched carrying a shipment of parcels to be delivered to customers. While the truck is following a predetermined route, a drone is charged with making the last-stretch delivery of a parcel from the truck to a customer's doorstep. Given a set of customers to be served and a set of rendezvous points where the drone can meet with the truck to pick up a parcel, we ask what the quickest way is of delivering all parcels to the end customers. We model this problem as a problem of finding a special type of a path in a graph of a special structure, and show that the graph problem is NP-hard even when all edge weights are restricted to be 1 or 2. Furthermore, we identify a special instance type that can be solved optimally in polynomial time. Finally, we propose a polynomial-time approximation algorithm for the graph problem in metric graphs, and show that its approximation ratio is bounded above by 2 in restricted metric graphs.", "keywords": [ "Truck", "Drone", "Computer science", "Bounded function", "Metric (unit)", "Routing (electronic design automation)", "Rendezvous", "Set (abstract data type)", "Graph", "Mathematical optimization", "Computer network", "Theoretical computer science" ], "cited_by_count": 48, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.jstage.jst.go.jp/article/ipsjjip/25/0/25_655/_pdf" }, { "id": "https://openalex.org/W4391549660", "doi": "10.1109/mrs60187.2023.10416777", "title": "Optimizing Fuel-Constrained UAV-UGV Routes for Large Scale Coverage: Bilevel Planning in Heterogeneous Multi-Agent Systems", "year": 2023, "publication_date": "2023-12-04", "type": "conference-paper", "language": "en", "authors": [ "Md Safwan Mondal", "Subramanian Ramasamy", "James D. Humann", "Jean-Paul Reddinger", "James M. Dotterweich", "Marshal Childers", "Pranav A. Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": null, "publisher": null, "abstract": "Fast moving unmanned aerial vehicles (UAVs) are well suited for aerial surveillance, but are limited by their battery capacity. To increase their endurance, UAVs can be refueled on slow moving unmanned ground vehicles (UGVs). This cooperative routing of UAV-UGV multi-agent system to survey vast regions within their speed and fuel constraints is a computationally challenging problem, but can be simplified with heuristics. In this study, we utilize heuristic approaches to obtain feasible and near-optimal solutions to the problem, leveraging the fuel limitations of the UAV with the minimum set cover algorithm to identify the UGV refueling points. These refueling stops enable the allocation of mission points to the UAV and UGV. A standard traveling salesman formulation and a vehicle routing formulation with time windows, dropped visits, and capacity constraints are used to solve for the UGV and UAV route, respectively. Experimental validation on a small-scale testbed (http://tiny.cc/vancvz) underscores the effectiveness of our multi-agent approach.", "keywords": [ "Testbed", "Computer science", "Heuristics", "Unmanned ground vehicle", "Travelling salesman problem", "Heuristic", "Routing (electronic design automation)", "Scale (ratio)", "Real-time computing", "Simulation", "Artificial intelligence", "Embedded system" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/mrs60187.2023.10416777" }, { "id": "https://openalex.org/W4405597993", "doi": "10.1007/978-3-031-73365-9_12", "title": "Optimal Placement of Mobile Sensors for the Distance-Constrained Line Routing Problem", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Ерзин Адиль Ильясович", "Anzhela Shadrina" ], "first_author": "Ерзин Адиль Ильясович", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Routing (electronic design automation)", "Line (geometry)", "Computer network", "Mathematics", "Geometry" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-73365-9_12" }, { "id": "https://openalex.org/W7117143853", "doi": "10.3390/drones10010007", "title": "A Hybrid Strategy-Assisted Cooperative Vehicles–Drone Multi-Objective Routing Optimization Method for Last-Mile Delivery", "year": 2025, "publication_date": "2025-12-23", "type": "article", "language": "en", "authors": [ "Mingyuan Yang", "Bing Xue", "Rui Zhang", "Fuwang Dong" ], "first_author": "Mingyuan Yang", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Drones have emerged as critical infrastructure for enhancing logistics efficiency in the emerging low-altitude economy, particularly in collaborative vehicle–drone research. However, existing research often neglects the impact of fair task allocation on workload balance among formations in large-scale routing scenarios, which compromises service quality. To address this gap, we introduce the Multi-vehicle with drones Collaborative Routing Problem with Large-scale Packages (MCRPLP), formulated as a bi-objective model aiming to minimize both operational cost and workload imbalance. A Hybrid Strategy-assisted Multi-objective Optimization Algorithm (HSMOA) is developed to overcome the limitations of existing methods, which struggle with balancing solution quality and computational efficiency in solving large-scale routing. Based on a Non-dominated Sorting Genetic Algorithm (NSGA-II), the HSMOA integrates a heuristic task assignment strategy that greedily reassigns packages between adjacent clusters. Then, by integrating a Pareto-front superiority evaluation model, an elite individual supplement strategy is designed to dynamically prune sub-optimal solution subspaces while enhancing the search within high-quality Pareto-front subspaces in HSMOA. Extensive experiments demonstrate the effectiveness of HSMOA in terms of solution quality and computational efficiency compared to multiple state-of-the-art methods. Further sensitivity analysis and managerial insights derived from a real-world case are also provided to support practical logistics implementation.", "keywords": [ "Sorting", "Workload", "Task (project management)", "Genetic algorithm", "Routing (electronic design automation)", "Multi-objective optimization", "Heuristic", "Vehicle routing problem" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10010007" }, { "id": "https://openalex.org/W7160154228", "doi": "10.3390/systems14050491", "title": "Optimization of Transportation and Delivery Routes Under Regional Constraints: A Two-Stage Solution Model Based on SDVRP and Truck-Drone Collaboration", "year": 2026, "publication_date": "2026-04-30", "type": "article", "language": "en", "authors": [ "Weiquan Kong", "Senlai Zhu", "Gaoming Yu" ], "first_author": "Weiquan Kong", "venue": "Systems", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid development of e-commerce and the increasing complexity of urban logistics, traditional delivery methods face significant challenges due to regional traffic restrictions and congestion. This paper presents a two-stage optimization approach for urban delivery routing, integrating the Split Delivery Vehicle Routing Problem (SDVRP) and truck-drone collaboration to address these challenges. In the first stage, a transportation route optimization model based on SDVRP is proposed, which accounts for regional constraints and vehicle capacity limitations. The model allows for demand splitting, reducing the number of vehicles required and minimizing transportation costs. In the second stage, a truck-drone collaborative delivery model is introduced to handle the “last mile” distribution, where drones complement trucks by delivering to areas with restricted vehicle access. The optimization model aims to minimize overall delivery costs while ensuring timely service. An enhanced genetic algorithm is further developed to solve this complex, multi-constrained model. Experimental results show that the proposed collaborative strategy reduces delivery costs by over 10% compared to truck-only delivery, and the improved algorithm achieves a 4.77% average cost reduction over traditional approaches. This study provides valuable insights for optimizing urban logistics systems under regional constraints, offering both theoretical and practical contributions to smart logistics development.", "keywords": [ "Truck", "Vehicle routing problem", "Genetic algorithm", "Routing (electronic design automation)", "Traffic congestion", "City logistics", "Complement (music)", "Drone" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2079-8954/14/5/491/pdf?version=1779066150" }, { "id": "https://openalex.org/W7147508291", "doi": "10.1016/j.tre.2026.104825", "title": "Air-ground multimodal transport planning for joint passenger mobility and parcel delivery: integration of drones, aircraft, and ground vehicles", "year": 2026, "publication_date": "2026-03-31", "type": "article", "language": "en", "authors": [ "Yimeng Zhang", "Chenjie Yang", "Haoning Xi", "Songhan Peng", "Junjie Yang", "Mi Gan", "Xiaobo Liu", "Ruixue Ai" ], "first_author": "Yimeng Zhang", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "Drones and electric Vertical Takeoff and Landing aircraft (eVTOLs) offer potential to improve transport efficiency and flexibility by circumventing ground transport constraints such as traffic congestion and infrastructure limitations. However, aerial vehicles alone often cannot meet transport demands, necessitating integration with ground-based systems. To address this challenge, this study investigates the Integrated Air-Ground Multimodal Transport Planning Problem (IAG-MTPP) for joint passenger mobility and parcel delivery. The IAG-MTPP integrates fixed-route and flexible-route vehicles, accommodating operational scenarios involving eVTOL aircraft, drones, and ground vehicles. The proposed IAG-MTPP is formulated as a Mixed-Integer Linear Programming (MILP) model that optimizes multimodal operations to minimize transshipment costs, delay penalties, and carbon emissions, while also incorporating capital costs and battery-energy constraints for electric air and ground vehicles. The model is tailored to the complexities of air-ground vehicle routing, considering transfer operations, inter-modal coordination, and routing flexibility. It enables switches between parcel-only, passenger-only, and integrated operations, providing adaptability to evolving transport demands. To solve large-scale instances, we customize an Adaptive Large Neighborhood Search (ALNS) algorithm with insertion, removal, and swap operators, feasibility checks, and an operator-selection scheme. Benchmarking against a commercial exact solver and multiple heuristic algorithms demonstrates the robustness and scalability of the proposed ALNS. The effectiveness and applicability of the proposed model and algorithm are validated through numerical experiments in scenarios including emergency rescue operations in Jiuzhaigou and urban transport in Chengdu, China. The results demonstrate that the integrated air-ground system optimizes multimodal routing while reducing transport costs and improving service coverage compared to alternatives. The proposed ALNS algorithm solves large-scale instances efficiently where commercial exact solvers fail, supporting real-world deployment in large networks and high-demand settings. This study offers insights and guidelines to support efficient resource allocation in the rapidly evolving low-altitude economy.", "keywords": [ "Flexibility (engineering)", "Adaptability", "Transshipment (information security)", "Multimodal transport", "Routing (electronic design automation)", "Takeoff", "Stochastic programming", "Drone", "Bilevel optimization" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104825" }, { "id": "https://openalex.org/W4414647546", "doi": "10.1109/rusautocon65989.2025.11177314", "title": "Cluster Method of Optimal Vehicle Routing Using Geospatial Clustering", "year": 2025, "publication_date": "2025-09-07", "type": "conference-paper", "language": "en", "authors": [ "Alexander B. Filimonov", "Н. Б. Филимонов" ], "first_author": "Alexander B. Filimonov", "venue": null, "publisher": null, "abstract": "The article considers one of the urgent and key problems of the transport industry related to the planning of routes for transport vehicles (TV). This problem can be described and formalized as the traveling salesman problem (TSP), which consists in finding the optimal route for a traveling salesman to visit all the specified cities once and then return to the source city. Multiple Traveling Salesman Problem (MTSP) allows more than one traveling salesman and more than one depot - their base location, where routes begin and end. TSP belongs to the class of NP-hard problems. One of the heuristic methods for its solving, which is described in this article, is based on geospatial clusterization of the initial set of cities. A two-stage CFRS (\"Cluster First - Route Second\") method for solving this problem is discussed, which allows reducing it to TSP family with a single depot. At the first stage, geospatial clusterization of cities is carried out and clusterization using the K-means method is used. At the second stage a single depot with vehicles designed to serve this cluster is selected for each cluster and routes to by bypass all cities in this cluster are built. As an example illustrating the effectiveness of the proposed solutions a numerical example of aviation logistics is considered - flight routing of multiple transport UAV with several depots based on them.", "keywords": [ "Travelling salesman problem", "Geospatial analysis", "Cluster analysis", "Heuristic", "Vehicle routing problem", "Set (abstract data type)", "Routing (electronic design automation)", "2-opt" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/rusautocon65989.2025.11177314" }, { "id": "https://openalex.org/W2009945360", "doi": "10.1109/mra.2010.938844", "title": "Today's Traveling Salesman Problem", "year": 2010, "publication_date": "2010-12-01", "type": "article", "language": "en", "authors": [ "Paul Oberlin", "Sivakumar Rathinam", "Swaroop Darbha" ], "first_author": "Paul Oberlin", "venue": "IEEE Robotics & Automation Magazine", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Heterogeneous unmanned aerial vehicles (UAVs) are being developed for several civil and military applications. These vehicles can differ either in their motion constraints or sensing/attack capabilities. This article uses methods from operations research to address a fundamental routing problem involving heterogeneous UAVs. The approach is to transform the routing problem into a relatively better understood single, asymmetric, traveling salesman problem (ATSP) and use the algorithms available for the ATSP to address the routing problem. To test the effectiveness of the transformation, the well-known Lin-Kernighan-Helsgaun heuristic was applied to the transformed ATSP. Computational results on the transformed ATSP show that solutions whose costs are within 16% of the optimum can be obtained relatively fast [within 40 s of central processing unit (CPU)] for the routing problem involving ten heterogeneous UAVs and 40 targets.", "keywords": [ "Travelling salesman problem", "Routing (electronic design automation)", "Heuristic", "2-opt", "Computer science", "Transformation (genetics)", "Traveling purchaser problem", "Mathematical optimization", "Vehicle routing problem", "Mathematics", "Computer network", "Algorithm" ], "cited_by_count": 104, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/mra.2010.938844" }, { "id": "https://openalex.org/W4414538735", "doi": "10.1109/icc52391.2025.11161267", "title": "UAV Aided Smart Agriculture Networks: A Multi-Agent Reinforcement Learning Approach", "year": 2025, "publication_date": "2025-06-08", "type": "conference-paper", "language": "en", "authors": [ "Guojun Xiong", "Jianlin Guo", "Kieran Parsons", "Yukimasa Nagai", "Takenori Sumi", "Philip Orlik", "Jian Li" ], "first_author": "Guojun Xiong", "venue": null, "publisher": null, "abstract": "This paper explores the transformative potential of the IoT paradigm in promoting smart agriculture. Key challenges lie in how to connect agriculture sensors to remote cloud servers in the absence of feasible communication infrastructure and the unreliable wireless links in rural areas. To address these issues, we propose an innovative two-tier smart agriculture architecture: an Unmanned Aerial Vehicle (UAV) aided agriculture network model, which leverages UAVs as intermediaries to collect and route data from agriculture sensors to cloud servers. This novel architecture leads to two particular problems, i.e., data packet scheduling in the first-tier networks and multi-hop routing in the second-tier UAV mesh network. To that end, we present formal Markov decision process (MDP) based problem formulations for both tiers, with a primary focus on the more challenging multi-hop routing problem in the second-tier network. This problem is approached as a multi-agent reinforcement learning (MARL) framework, for which we introduce a novel distributed algorithm - Focus Coordination: attention-guided Multi-Agent Deep Deterministic Policy Gradient (FC-MADDPG). This algorithm reduces communication overhead and mitigates the risks associated with single-node failures. We evaluated the performance of the proposed FC-MADDPG algorithm, demonstrating its efficacy in enhancing data transmission reliability and efficiency.", "keywords": [ "Reinforcement learning", "Cloud computing", "Markov decision process", "Scheduling (production processes)", "Server", "Overhead (engineering)", "Reliability (semiconductor)", "Key (lock)" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icc52391.2025.11161267" }, { "id": "https://openalex.org/W2069760152", "doi": "10.1109/ccdc.2009.5192623", "title": "An improved ant colony algorithm for UAV route planning in complex battlefield environment", "year": 2009, "publication_date": "2009-06-01", "type": "conference-paper", "language": "en", "authors": [ "Fei Su", "Yuan Li", "Lincheng Shen" ], "first_author": "Fei Su", "venue": null, "publisher": null, "abstract": "Ant colony algorithm is applied to solve UAV (Unmanned Aerial Vehicle) route planning in complex battle field environment. According to the properties of battlefield, an integrated cost estimate measure is presented, which takes the distance of UAV route and threats into account. Then a ant colony algorithm for UAV route planning is put forward to solve the problem. According to the characteristics of the UAV route planning in complex battlefield environment, state transition rules based on integrated cost estimation is designed, and the pheromone diffuse mechanism is introduced to improve the performance of algorithm. The simulation results demonstrate the feasibility and efficiency of our algorithm.", "keywords": [ "Ant colony optimization algorithms", "Battlefield", "Computer science", "Field (mathematics)", "Measure (data warehouse)", "Ant colony", "State (computer science)", "Algorithm", "Real-time computing", "Artificial intelligence", "Data mining", "Mathematics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccdc.2009.5192623" }, { "id": "https://openalex.org/W4406183815", "doi": "10.1007/s10696-024-09589-z", "title": "An approximation algorithm for mobile multi-agent monitoring and routing problem", "year": 2025, "publication_date": "2025-01-08", "type": "article", "language": "en", "authors": [ "Gwang Kim", "Yoonjea Jeong" ], "first_author": "Gwang Kim", "venue": "Flexible Services and Manufacturing Journal", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Mathematical optimization", "Routing (electronic design automation)", "Heuristic", "Submodular set function", "Patrolling", "Process (computing)", "Maximization", "Artificial intelligence", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10696-024-09589-z" }, { "id": "https://openalex.org/W3118862855", "doi": "10.1080/10556788.2022.2053972", "title": "Length-constrained cycle partition with an application to UAV routing*", "year": 2022, "publication_date": "2022-05-12", "type": "article", "language": "en", "authors": [ "Kai Hoppmann‐Baum", "Oleg P. Burdakov", "Gioni Mexi", "Carl Johan Casselgren", "Thorsten Koch" ], "first_author": "Kai Hoppmann‐Baum", "venue": "Optimization methods & software", "publisher": "Taylor & Francis", "abstract": "This article discusses the Length-Constrained Cycle Partition Problem (LCCP), which constitutes a new generalization of the Travelling Salesperson Problem (TSP). Apart from nonnegative edge weights, the undirected graph in LCCP features a nonnegative critical length parameter for each vertex. A cycle partition, i.e. a vertex-disjoint cycle cover, is a feasible solution for LCCP if the length of each cycle is not greater than the critical length of each vertex contained in it. The goal is to find a feasible partition having a minimum number of cycles. Besides analyzing theoretical properties and developing preprocessing techniques, we propose an elaborate heuristic algorithm that produces solutions of good quality even for large-size instances. Moreover, we present two exact mixed-integer programming formulations (MIPs) for LCCP, which are inspired by well-known modeling approaches for TSP. Further, we introduce the concept of conflict hypergraphs, whose cliques yield valid constraints for the MIP models. We conclude with a discussion on computational experiments that we conducted using (A)TSPLIB-based problem instances. As a motivating example application, we describe a routing problem where a fleet of uncrewed aerial vehicles (UAVs) must patrol a given set of areas.", "keywords": [ "Partition (number theory)", "Computer science", "Mathematics", "Combinatorics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/10556788.2022.2053972" }, { "id": "https://openalex.org/W2982078879", "doi": "10.1109/vnc48660.2019.9062772", "title": "Unmanned Aerial Vehicles in Logistics: Efficiency Gains and Communication Performance of Hybrid Combinations of Ground and Aerial Vehicles", "year": 2019, "publication_date": "2019-12-01", "type": "conference-paper", "language": "en", "authors": [ "Manuel Patchou", "Benjamin Sliwa", "Christian Wietfeld" ], "first_author": "Manuel Patchou", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs) have drastically gained popularity in various Intelligent Transportation System (ITS) applications to improve the safety and efficiency of transportation systems. In this context, the combination of ground vehicles, such as delivery trucks, with drones to assist in the last-mile pick-up and delivery of the parcels has been recently proposed. While aerial vehicles promise increased efficiency based on flexible routes and parallelized operation, highly reliable wireless communication is also required for the control and coordination of potentially many drones acting in a self-organized way. In this paper, we analyze the improvements procured by drone usage in parcel delivery compared to traditional delivery and propose a simulation framework to further quantify the efficiency gains of the parcel delivery logistics and to analyze the performance of different wireless communications options. To this end, we consider a heterogeneous vehicle routing problem with various constraints. We consider two approaches regarding the dispatching and recovery of drones and evaluate their benefits as opposed to parcel delivery with a classic truck only. Furthermore, we compare two networking technologies for enabling coordination of the self-organizing teams of drones with a realistically modeled environment: one approach relying on base station oriented Long Term Evolution (LTE) vs. a more decentralized Cellular Vehicle-to-Everything (C-V2X) solution. The results show time savings of nearly 80% can be achieved through drone usage and that the negative impact of urban shadowing on network communications in the base station oriented LTE approach can be compensated by leveraging decentralized C-V2X communications.", "keywords": [ "Drone", "Base station", "Truck", "Computer science", "Context (archaeology)", "Wireless", "Popularity", "Computer network", "Last mile (transportation)", "Transport engineering", "Distributed computing", "Telecommunications" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1910.10451" }, { "id": "https://openalex.org/W2357393533", "doi": null, "title": "Model and simulation of cooperative multi-task assignment for multi-UAV system", "year": 2011, "publication_date": "2011-01-01", "type": "article", "language": "en", "authors": [ "Long Guo-qing", "Xiaoping Zhu", "Zhou Zhou" ], "first_author": "Long Guo-qing", "venue": "Flight Dynamics", "publisher": "Princeton University Press", "abstract": "Aiming to find a solution,the cooperative multi-task assignment problem(CMTAP) for multi-UAV system is modeled based on the multi-depots vehicle routing problem with multiple constraints(MDVRPMC).After a genetic simulated annealing algorithm(GSA) based Pareto optimality is proposed,the cooperative multi-task assignment problem with multiple constraints is solved.The simulation results demonstrate the feasibility and efficiency of the method.", "keywords": [ "Simulated annealing", "Task (project management)", "Mathematical optimization", "Assignment problem", "Computer science", "Pareto principle", "Genetic algorithm", "Vehicle routing problem", "Multi-objective optimization", "Pareto optimal", "Routing (electronic design automation)", "Algorithm" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-FHLX201104018.htm" }, { "id": "https://openalex.org/W4327594614", "doi": "10.3390/drones7030203", "title": "Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas", "year": 2023, "publication_date": "2023-03-16", "type": "article", "language": "en", "authors": [ "Hannes Braßel", "Thomas Zeh", "Hartmut Fricke", "Anette Eltner" ], "first_author": "Hannes Braßel", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With unmanned aerial vehicle(s) (UAV), swift responses to urgent needs (such as search and rescue missions or medical deliveries) can be realized. Simultaneously, legislators are establishing so-called geographical zones, which restrict UAV operations to mitigate air and ground risks to third parties. These geographical zones serve particular safety interests but they may also hinder the efficient usage of UAVs in time-critical missions with range-limiting battery capacities. In this study, we address a facility location problem for up to two UAV hangars and combine it with a routing problem of a standard UAV mission to consider geographical zones as restricted areas, battery constraints, and the impact of wind to increase the robustness of the solution. To this end, water rescue missions are used exemplary, for which positive and negative location factors for UAV hangars and areas of increased drowning risk as demand points are derived from open-source georeferenced data. Optimum UAV mission trajectories are computed with an A* algorithm, considering five different restriction scenarios. As this pathfinding is very time-consuming, binary occupancy grids and image-processing algorithms accelerate the computation by identifying either entirely inaccessible or restriction-free connections beforehand. For the optimum UAV hangar locations, we maximize accessibility while minimizing the service times to the hotspots, resulting in a decrease from the average service time of 570.4 s for all facility candidates to 351.1 s for one and 287.2 s for two optimum UAV hangar locations.", "keywords": [ "Computer science", "Drone", "Robustness (evolution)", "Limiting", "Occupancy", "Georeference", "Real-time computing", "Service (business)", "Operations research", "Simulation", "Engineering", "Geography" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/7/3/203/pdf?version=1678952805" }, { "id": "https://openalex.org/W4297798716", "doi": "10.1109/icc45855.2022.9882288", "title": "Multi-Agent Learning Based Packet Routing in Multi-Hop UAV Relay Network", "year": 2022, "publication_date": "2022-05-16", "type": "conference-paper", "language": "en", "authors": [ "Jiawei Chen", "Ruijin Ding", "Wen Rong Wu", "Jun Liu", "Feifei Gao", "Xuemin Shen" ], "first_author": "Jiawei Chen", "venue": null, "publisher": null, "abstract": "In this paper, we investigate the packet routing problem in a multi-hop unmanned aerial vehicles (UAV) relay network, where multiple UAVs serve as relays between a base station (BS) and remote ground users (GUs) for enhancing network throughput. The challenges are that the dynamic network topology due to UAV mobility leads to volatile wireless connection. Moreover, there exists strong interference among UAVs due to line-of-sight communication links. Towards this end, we propose a novel multi-agent deep reinforcement learning based algorithm, named as MAQMIX for: 1) designing proper UAVs’ trajectories to provide reliable network connection between the BS and remote GUs; 2) properly allocating frequency resource among UAVs to alleviate interference; 3) choosing a proper next-hop UAV for each data packet. Specifically, two training mechanisms are incorporated in the MAQMIX, i.e., intra UAV and inter UAV training mechanisms, which can tackle large action space issue and coordinate the training among UAVs, respectively. Simulation results show that the MAQMIX can significantly outperform baseline schemes in terms of transmission time and network throughput.", "keywords": [ "Computer science", "Computer network", "Relay", "Network packet", "Reinforcement learning", "Base station", "Hop (telecommunications)", "Routing table", "Throughput", "Network topology", "Packet forwarding", "Real-time computing" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icc45855.2022.9882288" }, { "id": "https://openalex.org/W4412939677", "doi": "10.1109/jiot.2025.3595102", "title": "ccDNCA: A Dual-Neighborhood Search-Based Dual-Population Coevolutionary Algorithm for Multi-UAV Task Allocation Problems With Complex Constraints", "year": 2025, "publication_date": "2025-08-04", "type": "article", "language": "en", "authors": [ "Xi Chen", "Zipeng Zhao", "Yu Wan", "Jingtao Qi", "Yirun Ruan", "Xin Biao Lu", "Jun Tang" ], "first_author": "Xi Chen", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Solving the multi-UAV task allocation problem with complex constraints (MTAPCc) by means of the constrained multi-objective evolutionary algorithms (cMOEAs) is novel research in the field of Operation Research. Its advantages mainly consist of two aspects. One is that it can find feasible solutions that satisfy the constraints within an acceptable time. The other is that the obtained Pareto solution set can offer more options for decision-makers. This paper presents a dualneighborhood search based dual-population coevolutionary algorithm (ccDNCA), which can specifically solve the constrained multi-objective combinatorial optimization problems (cMCOPs) based on permutation encoding, including the MTAPCc. The dual-population coevolutionary framework and the multistrategy collaborative constraint handling method of ccDNCA can effectively improve the efficiency of constraint handling and the ability of finding better solutions. The dual-neighborhood alternating local search (DN-ALS) framework can effectively increase the proportion of feasible solutions during the evolution and enhance the quality of the final solution set. The strategy pool integrated with multiple local search strategies can push the search towards regions with better objective values and constraint values, while enhancing the generalization ability of ccDNCA. In the experimental part, by comprehensively comparing the solution results of ccDNCA with those of other advanced algorithms, it is demonstrated that ccDNCA has significant superiority when dealing with cMCOPs based on permutation encoding, such as the MTAPCc and the Vehicle Routing Problem with Time Window constraints (VRPTW).", "keywords": [ "Dual (grammatical number)", "Computer science", "Task (project management)", "Population", "Mathematical optimization", "Algorithm", "Artificial intelligence", "Mathematics" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2025.3595102" }, { "id": "https://openalex.org/W3187485481", "doi": "10.1109/fuzz45933.2021.9494491", "title": "Periodic planning of UAVs' fleet mission with the uncertainty of travel parameters", "year": 2021, "publication_date": "2021-07-11", "type": "conference-paper", "language": "en", "authors": [ "Grzegorz Radzki", "Grzegorz Bocewicz", "Paulina Golińska-Dawson", "Małgorzata Jasiulewicz–Kaczmarek", "Marcin Witczak", "Zbigniew Antoni Banaszak" ], "first_author": "Grzegorz Radzki", "venue": null, "publisher": null, "abstract": "This paper presents a variant of the Periodic Vehicle Routing Problem, where a fleet of homogeneous Unmanned Aerial Vehicles (UAVs) in periodically repeated missions, serves spatially dispersed customers, over a planning horizon. The plan of mission includes both routing and scheduling decisions. The fleet's size and UAV's lifting capacity allow for the implementation of missions. However, the changing weather conditions affect the energy consumption of UAVs, and limits their range because of an earlier depletion of batteries. That may result in only partly fulfilment of the demand. Thus, the planning of subsequent missions should take into consideration, previously unmet demand, as well as the current weather forecast. We aim to propose a new approach to an online, proactive periodic planning, which guarantees timely delivery of the required quantity of goods, while taking into account the forecasted weather changes. We implement a constraint programming approach with ordered fuzzy numbers. That allows coping with nonlinearity of system's characteristics and computational accumulation of inaccuracies generated by the uncertainty of travel parameters. The proposed approach is evaluated on several instances, including real-life scenarios. The computational experiments have shown that the developed model is capable of providing feasible plans of UAVs' mission in an online mode.", "keywords": [ "Computer science", "Operations research", "Scheduling (production processes)", "Time horizon", "Route planning", "Routing (electronic design automation)", "Range (aeronautics)", "Real-time computing", "Simulation", "Transport engineering", "Mathematical optimization", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/fuzz45933.2021.9494491" }, { "id": "https://openalex.org/W2042243514", "doi": "10.21236/ada380543", "title": "Dynamic Routing of Unmanned Aerial Vehicles Using Reactive Tabu Search", "year": 1999, "publication_date": "1999-11-26", "type": "report", "language": "en", "authors": [ "Kevin P. O’Rourke", "T. G. Bailey", "Raymond R. Hill", "William B. Carlton" ], "first_author": "Kevin P. O’Rourke", "venue": null, "publisher": null, "abstract": "In this paper we consider the dynamic routing of unmanned aerial vehicles (UAVs) currently in operational use with the US Air Force. Dynamic vehicle routing problems (VRP) have always been challenging, and the airborne version of the VRP adds dimensions and difficulties not present in typical ground-based applications. Previous UAV routing work has focused on primarily on static, pre-planned situations; however, scheduling military operations, which are often ad-hoc, drives the need for a dynamic route solver that can respond to rapidly evolving problem constraints. With these considerations in mind, we examine the use of a Java-encoded metaheuristic to solve these dynamic routing problems, explore its operation with several general problem classes, and look at the advantages it provides in sample UAV routing problems. The end routine provides routing information for a UAV virtual battlespace simulation and allows dynamic routing of operational missions.", "keywords": [ "Tabu search", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Artificial intelligence", "Computer network" ], "cited_by_count": 46, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.21236/ada380543" }, { "id": "https://openalex.org/W4296545377", "doi": "10.48550/arxiv.2209.08533", "title": "RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System", "year": 2022, "publication_date": "2022-09-18", "type": "preprint", "language": "en", "authors": [ "Boyu Zhou", "Hao Xu", "Shaojie Shen" ], "first_author": "Boyu Zhou", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this paper, we present RACER, a RApid Collaborative ExploRation approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Further, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in real world. We will release our implementation as an open-source package.", "keywords": [ "Scalability", "Computer science", "Robustness (evolution)", "Asynchronous communication", "Distributed computing", "Planner", "Viewpoints", "Pairwise comparison", "Real-time computing", "Artificial intelligence", "Computer network", "Database" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2209.08533" }, { "id": "https://openalex.org/W4410770201", "doi": "10.23919/jsee.2025.000048", "title": "Aerial-Ground Collaborative Delivery Route Planning with UAV Energy Function and Multi-Delivery", "year": 2025, "publication_date": "2025-04-01", "type": "article", "language": "en", "authors": [ "Jingfeng Guo", "Rui Song", "Shiwei He" ], "first_author": "Jingfeng Guo", "venue": "Journal of Systems Engineering and Electronics", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "With the rapid development of low-altitude economy and unmanned aerial vehicles (UAVs) deployment technology, aerial-ground collaborative delivery (AGCD) is emerging as a novel mode of last-mile delivery, where the vehicle and its onboard UAVs are utilized efficiently. Vehicles not only provide delivery services to customers but also function as mobile warehouses and launch/recovery platforms for UAVs. This paper addresses the vehicle routing problem with UAVs considering time window and UAV multi-delivery (VRPU-TW&MD). A mixed integer linear programming (MILP) model is developed to minimize delivery costs while incorporating constraints related to UAV energy consumption. Subsequently, a micro-evolution augmented large neighborhood search (MEALNS) algorithm incorporating adaptive large neighborhood search (ALNS) and micro-evolution mechanism is proposed. Numerical experiments demonstrate the effectiveness of both the model and algorithm in solving the VRPU-TW&MD. The impact of key parameters on delivery performance is explored by sensitivity analysis.", "keywords": [ "Function (biology)", "Energy (signal processing)", "Delivery system", "Integrated project delivery", "Computer science", "Environmental science", "Engineering", "Civil engineering", "Physics", "Biomedical engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/jsee.2025.000048" }, { "id": "https://openalex.org/W4316658513", "doi": "10.1109/icaiss55157.2022.10010870", "title": "A Critical Simulated Analysis of 5G based Aerial Vehicle Routings using NS3", "year": 2022, "publication_date": "2022-11-24", "type": "conference-paper", "language": "en", "authors": [ "Hemant Kumar Saini", "Gurleen Kaur", "Kusum Lata Jain" ], "first_author": "Hemant Kumar Saini", "venue": "2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)", "publisher": null, "abstract": "In this paper a quick survey and similar investigation of the adhoc steering conventions utilized in aerial network will be introduced. The exploration documented in airborne adhoc network (AANET) is growing exceptionally quick. A broad assortment of usages has been served under different circumstances (Roadway, metropolitan, and Urban communities). Numerous conventions have been taken on to serve different geography and situations, these conventions confronted different difficulties. AANETs give correspondence between vehicles continuing on the air. The steering conventions in AANET are impacted by the vehicle fast which prompts oftentimes connect breaks between the conveyed vehicles, so the adhoc directing conventions are adjusted with the AANET attributes to convey the information between vehicles in brief time frame. The fundamental objective of AANET is to collect an information framework among vehicles that are continuing broadcasting in real time, which empowers the vehicles to speak with one another for the security habits. In this paper, an undertaking has been made to think about the aerial vehicles in both 3D and 2D situations which without a doubt comprehended conventions AODV, DSR, OLSR, DSDV utilized in AANET according to the viewpoint of PDR, end to end delay, throughput, signifies the suitability of uavs in 3D and also proven the best routing for flight performance is by AODV. This effects a great deal in helping the 5G correspondence at high elevations with same environmental factors of UAVs to keep up with the organization.", "keywords": [ "Computer science", "Metropolitan area", "Wireless ad hoc network", "Identifier", "Vehicular ad hoc network", "Frame (networking)", "Transport engineering", "Throughput", "Mobile ad hoc network", "Broadcasting (networking)", "Operations research", "Computer security" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/icaiss55157.2022.10010870" }, { "id": "https://openalex.org/W4386913035", "doi": "10.1007/s10601-023-09355-2", "title": "Constraint programming approaches to electric vehicle and robot routing problems", "year": 2023, "publication_date": "2023-09-01", "type": "article", "language": "en", "authors": [ "Kyle E. C. Booth" ], "first_author": "Kyle E. C. Booth", "venue": "Constraints", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Electric vehicle", "Greenhouse gas", "Robot", "Mobile robot", "Computer science", "Battery (electricity)", "Variety (cybernetics)", "Investment (military)", "Electric motor", "Vehicle routing problem", "Automotive engineering", "Routing (electronic design automation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://utoronto.scholaris.ca/bitstreams/3265fa87-3652-4c14-917a-8c6e4adfa2c6/download" }, { "id": "https://openalex.org/W4221158956", "doi": "10.48550/arxiv.2203.04595", "title": "Practical Mission Planning for Optimized UAV-Sensor Wireless Recharging", "year": 2022, "publication_date": "2022-03-09", "type": "preprint", "language": "en", "authors": [ "Qiuchen Qian", "O'Keeffe, James", "Yanran Wang", "Boyle, David" ], "first_author": "Qiuchen Qian", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Optimal maintenance of sensor nodes in a Wireless Rechargeable Sensor Network (WRSN) requires effective scheduling of power delivery vehicles by solving the Charging Scheduling Problem (CSP). Deploying Unmanned Aerial Vehicles (UAVs) as mobile chargers has emerged as a promising solution due to their mobility and flexibility. The CSP can be formulated as a Mixed-Integer Non-Linear Programming problem whose optimization objective is maximizing the recharged energy of sensor nodes within the UAV battery constraint. While many studies have demonstrated satisfactory performance of heuristic algorithms in addressing specific routing problems, few studies explore online updating (i.e., mission re-planning `on the fly') in the CSP context. Here we present a new offline and online mission planner leveraging a first-principles power consumption model that uses real-time state information and environmental information. The planner, namely Rapid Online Metaheuristic-based Planner (ROMP), supplements solutions from a Guided Local Search (GLS) with our Context-aware Black Hole Algorithm. Our results demonstrate that ROMP outperforms GLS in most cases tested. We developed and proposed FastROMP to speed up the online mission (re-)planning algorithm by introducing a new online adjustment operator that uses the latest state information as input, eliminating the need for re-initialization. FastROMP not only provides a better quality route, but it also significantly reduces computational time. The reduction ranges from 39.57% in sparse deployment to 93.3% in denser deployments.", "keywords": [ "Computer science", "Wireless sensor network", "Real-time computing", "Scheduling (production processes)", "Context (archaeology)", "Energy consumption", "Software deployment", "Job shop scheduling", "Mathematical optimization", "Distributed computing", "Routing (electronic design automation)", "Computer network" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2203.04595" }, { "id": "https://openalex.org/W641809592", "doi": null, "title": "Unmanned Aerial System Routing Problem Dynamic Solution Methods and Literature Review", "year": 2013, "publication_date": "2013-06-09", "type": "article", "language": "en", "authors": [ "Cihan Ercan", "Cevriye Gencer" ], "first_author": "Cihan Ercan", "venue": "DergiPark (Istanbul University)", "publisher": "Istanbul University", "abstract": "War tools and materials, which are an indicator of war strategy and development, vary with the technology. Modern Warfares in the information age, where near space and the space being used as a tool, differ from both the agricultural era wars, in which arrows and bows had been used as a tool, and industrial era battles, in which machine guns and tanks were employed. These characteristics influence this area of interest and area of influence in the area of responsibilities, and affect the ability and the necessity of modern armies as well. Being able to perform reconnaissance and surveillance missions in the most effective and efficient way is reliant on not only having the knowledge to create and invent them as well as having them in the inventor but also the ability of planning these systems with a modern scientific approach. Vehicle Routing Problem (VRP), encountered during the assignment of Unmanned Aerial Systems\\Vehicles (UAS\\V) which is being used at the strategic level, can lead to elevated costs in the defense sector as well as other sectors. For this reason, the efficient solution of route planning is a very important issue to provide major cost savings and to observe the targets timely. This study investigates the literature in “dynamic” route planning “solution” methods and defines the approaches for future “dynamic solution” studies of strategic UAVs which are being recently used in Turkey. Using this approach will increase the efficiency of usage of the UAVs and decrease the operating and project costs of them as well.", "keywords": [ "Space (punctuation)", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Operations research", "Risk analysis (engineering)", "Transport engineering", "Engineering", "Business" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/tr/pub/sujest/issue/23218/247938" }, { "id": "https://openalex.org/W4286850438", "doi": "10.48550/arxiv.2111.13050", "title": "The vehicle routing problem with drones and drone speed selection", "year": 2021, "publication_date": "2021-11-25", "type": "preprint", "language": "en", "authors": [ "Felix Tamke", "Udo Buscher" ], "first_author": "Felix Tamke", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Joint parcel delivery by trucks and drones has enjoyed significant attention for some time, as the advantages of one delivery method offset the disadvantages of the other. This paper focuses on the vehicle routing problem with drones and drone speed selection (VRPD-DSS), which considers speed-dependent energy consumption and drone-charging in detail. For this purpose, we formulate a comprehensive mixed-integer problem that aims to minimize the operational costs consisting of fuel consumption costs of the trucks, labor costs for the drivers, and energy costs of the drones. The speed at which a drone performs a flight must be selected from a discrete set. We introduce preprocessing steps to eliminate dominated speeds for a flight to reduce the problem size and use valid inequalities to accelerate the solution process. The consideration of speed-dependent energy consumption leads to the fact that it is advisable to perform different flights at different speeds and not to consistently operate a drone at maximum speed. Instead, drone speed should be selected to balance drone range and speed of delivery. Our extensive computational study of a rural real-world setting shows that, by modeling energy consumption realistically, the savings in operational costs compared to truck-only delivery are significant but smaller than those identified in previously published work. Our analysis further reveals that the greatest savings stem from the fact that overall delivery time decreases compared to truck-only delivery, allowing costly truck-driver time to be reduced. The additional energy costs of the drone, however, are largely negligible.", "keywords": [ "Drone", "Truck", "Energy consumption", "Computer science", "Fuel efficiency", "Routing (electronic design automation)", "Operations research", "Transport engineering", "Engineering", "Automotive engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2111.13050" } ]